How To Access Information About PDF Data Scraping?

Scraping a way that the output of data from another program to extract data is used by a computer program can be heard. Simply put, this is a process of automatically sorting the information from the Internet, even within an HTML file can be found in various sources, including PDF documents and others. There is also a collection of relevant information. This information to the database or spreadsheet, allowing users to retrieve them later will be included.

Most websites today can be viewed and written text in the source code is simple. However, there are other companies that currently use Adobe PDF or Portable Document Format to choose from. This file is a type known as just the free Adobe Acrobat to be viewed using the software. Supports virtually all operating software, said. There are many advantages when you choose to create PDF files. Those document you just the same, even if you put it in another computer, so you can see it look. Therefore, business documents or completes the data sheet. Of course there are drawbacks. One of these is included in the text is converted into an image. In this case, it is often the problem with this is that when it comes to copy and paste, and could be.

That's why some are starting to scrape the information PDF. It is often said that the only scraping process information in your PDF file PDF is like to get data. PDF to start scraping the information from you, choose a device specially designed for this process must benefit. However, you feel that you have the right tools too effectively scrape PDF will be able to perform is not easy to detect. This is because the equipment is exactly the same data access without having personal problems.

However, if you look good, you look at programs that you may encounter. You have to know programming; you do not need to use them. You can easily specify their preferences for the software you use will do the rest. There are companies out there that you contact them and they work because they have the right tools they can use to be. If you choose to do things yourself, you will find it really difficult and complicated compared to professionals working for you, they will at no time possible. PDF scraping of information is a process whereby information can be found on the Internet and not copyright infringement to collect.

Well I hope you now understand how to scrape data in various forms. If you do not understand then go for one of the sites I mention below in the box of the author. We offer a variety of data services, such as HTML scraping services, the crop Scraping Web Services Web Content, Email Id scraping, scraping data ownership, data Linkedin scraping, scraping data Hotels, pharmaceutical Scraping data, Business Contact Scraping, Data Scraping For University etc. If you have any doubts, please feel free to ask us without hesitation. We will certainly be useful for you. Thank you.

Source:http://www.articlesbase.com/outsourcing-articles/how-to-access-information-about-pdf-data-scraping-5293692.html

Scraping By

In his classic 1976 Chesapeake portrait, Beautiful Swimmers, William Warner described the scrape boat as "a workboat unlike any other I had ever seen on the Bay." Seeming half as wide as it was long, he said, it looked like a "a miniature battleship." There's a reason for that, of course. It's a classic case of form following function; the boat evolved for one purpose, to ply the Bay's grassy shallows for shedding blue crabs.

Said to "float on a heavy dew," scrape boats run from 26 to 30 feet long and 9 to 10 feet wide. The hull is a shallow-V deadrise that quickly flattens toward the stern, enabling the boat to pull its twin scrapes—rectangular steel frames, each with a trailing mesh bag—in knee-deep waters. The broad beam might sound ungainly, but the hull tapers toward the stern—betraying its sailboat origins. And it has a graceful sheer, flowing from a bow height of a few feet to little more than a foot above the water amidships.

And you want a low freeboard when you spend the whole day hoisting aboard scrapes, which weigh 50 pounds apiece, not including the load of sea grass and crabs that come in too. Low sides or not, there's a higher than average inci-dence of back problems among scrape boat crabbers. They spend long days bending in precisely the position back doctors say puts undue pressure on the lower back as they sort through rolls of grasses to pluck out the peelers and softies. And that alone may be why crab potting is now the far more common way of catching soft crabs.

Some people think that's good, assuming that dragging a scrape across the Bay's beleaguered grass flats must be destructive. But the smooth bar of the scrape, unlike a toothed dredge, doesn't uproot grasses. In fact, where scraping is traditional, the grass beds seem relatively resilient. I've often thought if Maryland and Virginia had stuck with scraping as the major legal way to soft-crab, overfishing might not have become a problem. Pots can be deployed everywhere and by the thousands, whereas scraping is limited to grass beds and to ground covered at three miles per hour; and even the sturdiest waterman can only pull two of them by hand. But peeler pots seem here to stay, and other soft crabbers have taken to using a single, large scrape operated from larger workboats by hydraulic power.

The bottom line is that these lovely, superbly functional expressions of Chesapeake crabbing culture now number only in the dozens, if you count working, wooden models. There are some fiberglass scrape boat hulls in service, and a Carolina skiff or two has been adapted for the task. They are functional, but have little art to them.

It is probably a sign of how fast scrape boats are going that the Smithsonian Institution recently took the lines off Darlene, a scraper worked by Morris Marsh of Smith Island, for its archives. You can see photos of scrape boats, and learn more about the 140-year old history of scraping, from Paula Johnson's fine book, The Workboats of Smith Island. Mr. Marsh, still going strong in his late 60s, is the scraper who took Warner out nearly 40 years ago when he was researching Beautiful Swimmers.

Indeed, scraping seems to win over those who master it. Marsh's father-in-law, Ed Harrison, scraped for almost 70 years, nearly wearing through the cross-planked bottom of his boat—from the inside—with decades of walking the planks, tending his scrapes. And an islander who scrapes with Marsh today, David Laird, says he is 71—one year younger than Scotty Boy, the scrape boat he took over from his dad in 1958. "I wouldn't even know how to crab in another boat," Laird says.

Soft crabs may well be caught—or farmed—a century from now on the Chesapeake; but no one will devise a way to take them so intimately and beautifully from the shallowest marsh edges and tiniest crevices in the shore as the scrapers do.

Source:http://www.articlesbase.com/culture-articles/scraping-by-1560919.html

Data Mining for Dollars

The more you know, the more you're aware you could be saving. And the deeper you dig, the richer the reward.

That's today's data mining capsulation of your realization: awareness of cost-saving options amid logistical obligations.

According to global trade group Association for Information and Image Management (AIIM), fewer than 25% of organizations in North America and Europe are currently utilizing captured data as part of their business process. With high ease and low cost associated with utilization of their information, this unawareness is shocking. And costly.

Shippers - you're in prime position to benefit the most by data mining and assessing your electronically-captured billing records, by utilizing a freight bill processing provider, to realize and receive significant savings.

Whatever your volume, the more you know about your transportation options, throughout all modes, the easier it is to ship smarter and save. A freight bill processor is able to offer insight capable of saving you 5% - 15% annually on your transportation expenditures.

The University of California - Los Angeles states that data mining is the process of analyzing data from different perspectives and summarizing it into useful information - knowledge that can be used to increase revenue, cuts costs, or both. Data mining software is an analytical tool that allows investigation of data from many different dimensions, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations among dozens of fields in large relational databases. Practically, it leads you to noticeable shipping savings.

Data mining and subsequent reporting of shipping activity will yield discovery of timely, actionable information that empowers you to make the best logistics decisions based on carrier options, along with associated routes, rates and fees. This function also provides a deeper understanding of trends, opportunities, weaknesses and threats. Exploration of pertinent data, in any combination over any time period, enables you the operational and financial view of your functional flow, ultimately providing you significant cost savings.

With data mining, you can create a report based on a radius from a ship point, or identify opportunities for service or modal shifts, providing insight regarding carrier usage by lane, volume, average cost per pound, shipment size and service type. Performance can be measured based on overall shipping expenditures, variances from trends in costs, volumes and accessorial charges.

The easiest way to get into data mining of your transportation information is to form an alliance with a freight bill processor that provides this independent analytical tool, and utilize their unbiased technologies and related abilities to make shipping decisions that'll enable you to ship smarter and save.

Source:http://ezinearticles.com/?Data-Mining-for-Dollars&id=7061178

Scraping table from any web page with R or CloudStat

Scraping table from any web page with R or CloudStat:

You need to use the data from internet, but don’t type, you can just extract or scrape them if you know the web URL.

Thanks to XML package from R. It provides amazing readHTMLtable() function.

For a study case,

I want to scrape data:

    US Airline Customer Score.
    World Top Chess Players (Men).

A. Scraping US Airline Customer Score table from

http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines

Code:

airline = ‘http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines’

airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)

Result:

> library(XML)

Warning message:

package "XML" was built under R version 2.14.1

> airline = "http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines"
> airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)
> airline.table

                     Base-line 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10
1          Southwest        78 76 76 76 74 72 70 70 74 75 73 74 74 76 79 81 79
2         All Others        NM 70 74 70 62 67 63 64 72 74 73 74 74 75 75 77 75
3           Airlines        72 69 69 67 65 63 63 61 66 67 66 66 65 63 62 64 66
4        Continental        67 64 66 64 66 64 62 67 68 68 67 70 67 69 62 68 71
5           American        70 71 71 62 67 64 63 62 63 67 66 64 62 60 62 60 63
6             United        71 67 70 68 65 62 62 59 64 63 64 61 63 56 56 56 60
7         US Airways        72 67 66 68 65 61 62 60 63 64 62 57 62 61 54 59 62
8              Delta        77 72 67 69 65 68 66 61 66 67 67 65 64 59 60 64 62
9 Northwest Airlines        69 71 67 64 63 53 62 56 65 64 64 64 61 61 57 57 61

  11 PreviousYear%Change FirstYear%Change

1 81                 2.5              3.8
3 65                -1.5             -9.7
4 64                -9.9             -4.5
5 63                 0.0            -10.0
7 61                -1.6            -15.3
8 56                -9.7            -27.3
9  #                 N/A              N/A

>

B. Scraping World Top Chess players (Men) table from http://ratings.fide.com/top.phtml?list=men

Code:

chess = ‘http://ratings.fide.com/top.phtml?list=men’
chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)

Result:

> chess = "http://ratings.fide.com/top.phtml?list=men"
> chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)
> chess.table

     Rank                       Name Title Country Rating Games B-Year

1      1           Carlsen, Magnus    g    NOR  2835   17  1990
2      2            Aronian, Levon    g    ARM  2805   25  1982
3      3         Kramnik, Vladimir    g    RUS  2801   17  1975
4      4        Anand, Viswanathan    g    IND  2799   17  1969
5      5         Radjabov, Teimour    g    AZE  2773    9  1987
6      6          Topalov, Veselin    g    BUL  2770    9  1975
7      7          Karjakin, Sergey    g    RUS  2769   16  1990
8      8         Ivanchuk, Vassily    g    UKR  2766   16  1969
9      9     Morozevich, Alexander    g    RUS  2763    6  1977
10    10           Gashimov, Vugar    g    AZE  2761    9  1986
11    11       Grischuk, Alexander    g    RUS  2761    8  1983
12    12          Nakamura, Hikaru    g    USA  2759   17  1987
13    13            Svidler, Peter    g    RUS  2749   17  1976
14    14    Mamedyarov, Shakhriyar    g    AZE  2747    9  1985
15    15       Tomashevsky, Evgeny    g    RUS  2740    0  1987
16    16            Gelfand, Boris    g    ISR  2739    9  1968
17    17          Caruana, Fabiano    g    ITA  2736   19  1992
18    18       Nepomniachtchi, Ian    g    RUS  2735   16  1990
19    19                 Wang, Hao    g    CHN  2733    6  1989
20    20              Kamsky, Gata    g    USA  2732    0  1974
21    21  Dominguez Perez, Leinier    g    CUB  2730    6  1983
22    22         Jakovenko, Dmitry    g    RUS  2729    0  1983
23    23        Ponomariov, Ruslan    g    UKR  2727   13  1983
24    24          Vitiugov, Nikita    g    RUS  2726    1  1987
25    25            Adams, Michael    g    ENG  2724   17  1971
26    26               Leko, Peter    g    HUN  2720    9  1979
27    27            Almasi, Zoltan    g    HUN  2717    8  1976
28    28               Giri, Anish    g    NED  2714   15  1994
29    29            Le, Quang Liem    g    VIE  2714    0  1991
30    30             Navara, David    g    CZE  2712    8  1985
31    31            Shirov, Alexei    g    LAT  2710   13  1972
32    32             Polgar, Judit    g    HUN  2710    0  1976
33    33     Riazantsev, Alexander    g    RUS  2710    0  1985
34    34       Wojtaszek, Radoslaw    g    POL  2706    8  1987
35    35      Moiseenko, Alexander    g    UKR  2706    7  1980
36    36   Vallejo Pons, Francisco    g    ESP  2705   15  1982
37    37        Malakhov, Vladimir    g    RUS  2705    0  1980
38    38            Jobava, Baadur    g    GEO  2704   23  1983
39    39           Bacrot, Etienne    g    FRA  2704   14  1983
40    40          Laznicka, Viktor    g    CZE  2704    8  1988
41    41            Sutovsky, Emil    g    ISR  2703    8  1977
42    42        Naiditsch, Arkadij    g    GER  2702   14  1985
43    43         Movsesian, Sergei    g    ARM  2700    9  1978
44    44       Sasikiran, Krishnan    g    IND  2700    9  1981
45    45   Vachier-Lagrave, Maxime    g    FRA  2699   13  1990
46    46            Dreev, Aleksey    g    RUS  2698    6  1969
47    47           Efimenko, Zahar    g    UKR  2695    8  1985
48    48         Volokitin, Andrei    g    UKR  2695    0  1986
49    49                 Wang, Yue    g    CHN  2694    6  1987
50    50        Fressinet, Laurent    g    FRA  2693   17  1981
51    51                Li, Chao b    g    CHN  2693    6  1989
52    52            Grachev, Boris    g    RUS  2693    0  1986
53    53      Nielsen, Peter Heine    g    DEN  2693    0  1973
54    54            Van Wely, Loek    g    NED  2692   13  1972
55    55    Bruzon Batista, Lazaro    g    CUB  2691   19  1982
56    56           McShane, Luke J    g    ENG  2691    8  1984
57    57            Eljanov, Pavel    g    UKR  2690   10  1983
58    58      Kasimdzhanov, Rustam    g    UZB  2689   14  1979
59    59         Inarkiev, Ernesto    g    RUS  2689    6  1985
60    60         Zvjaginsev, Vadim    g    RUS  2688    8  1976
61    61         Andreikin, Dmitry    g    RUS  2688    0  1990
62    62    Areshchenko, Alexander    g    UKR  2688    0  1986
63    63         Rublevsky, Sergei    g    RUS  2686    0  1974
64    64         Akopian, Vladimir    g    ARM  2685    8  1971
65    65          Potkin, Vladimir    g    RUS  2684    0  1982
66    66       Sargissian, Gabriel    g    ARM  2683   15  1983
67    67            Berkes, Ferenc    g    HUN  2682   16  1985
68    68           Bologan, Viktor    g    MDA  2680   15  1971
69    69          Bauer, Christian    g    FRA  2679   24  1977
70    70          Tiviakov, Sergei    g    NED  2677   22  1973
71    71            Short, Nigel D    g    ENG  2677   15  1965
72    72        Motylev, Alexander    g    RUS  2677    6  1979
73    73         Gharamian, Tigran    g    FRA  2676    0  1984
74    74          Kobalia, Mikhail    g    RUS  2673    0  1978
75    75              Meier, Georg    g    GER  2671    9  1987
76    76       Onischuk, Alexander    g    USA  2670   13  1975
77    77              Bu, Xiangzhi    g    CHN  2670    6  1985
78    78          Alekseev, Evgeny    g    RUS  2670    0  1985
79    79            Azarov, Sergei    g    BLR  2667    0  1983
80    80        Kryvoruchko, Yuriy    g    UKR  2666    0  1986
81    81             Balogh, Csaba    g    HUN  2665    8  1987
82    82           Harikrishna, P.    g    IND  2665    6  1986
83    83       Khismatullin, Denis    g    RUS  2664    8  1984
84    84   Nguyen, Ngoc Truong Son    g    VIE  2662    6  1990
85    85           Fridman, Daniel    g    GER  2660   11  1976
86    86              Smirin, Ilia    g    ISR  2660    7  1968
87    87               Ding, Liren    g    CHN  2660    6  1992
88    88         Sadler, Matthew D    g    ENG  2660    3  1974
89    89            Korobov, Anton    g    UKR  2660    0  1985
90    90          Cheparinov, Ivan    g    BUL  2659   18  1986
91    91          Timofeev, Artyom    g    RUS  2659    0  1985
92    92           Georgiev, Kiril    g    BUL  2658   17  1965
93    93           Bartel, Mateusz    g    POL  2658    9  1985
94    94          Zhigalko, Sergei    g    BLR  2658    8  1989
95    95         Feller, Sebastien    g    FRA  2658    0  1991
96    96            Ragger, Markus    g    AUT  2655   17  1988
97    97         Jones, Gawain C B    g    ENG  2653   27  1987
98    98                So, Wesley    g    PHI  2653    5  1993
99    99              Milov, Vadim    g    SUI  2653    0  1972
100  100           Gupta, Abhijeet    g    IND  2652    9  1989
101  101            Postny, Evgeny    g    ISR  2652    8  1981
102  102             Roiz, Michael    g    ISR  2652    6  1983
103  103           Gyimesi, Zoltan    g    HUN  2652    4  1977
104  104          Nikolic, Predrag    g    BIH  2652    2  1960

>

Done. You had successfully scraping data from any web page with R or CloudStat.

Then, you can analyze as usual! Great! No more retype the data. Enjoy!

Source: http://www.r-bloggers.com/scraping-table-from-any-web-page-with-r-or-cloudstat/

Basic Information About Tooth Extraction Cost

In order to maintain the good health of teeth, one must be devoted and must take proper care of one's teeth. Dentists play a huge role in this regard and their support is important in making people aware of their oral conditions, so that they receive the necessary health services concerning the problems of the mouth.

The flat fee of teeth-extraction varies from place to place. Nonetheless, there are still some average figures that people can refer to. Simple extraction of teeth might cause around 75 pounds, but if people need to remove the wisdom teeth, the extraction cost would be higher owing to the complexity of extraction involved.

There are many ways people can adopt in order to reduce the cost of extraction of tooth. For instance, they can purchase the insurance plans covering medical issues beforehand. When conditions arise that might require extraction, these insurance claims can take care of the costs involved.

Some of the dental clinics in the country are under the network of Medicare system. Therefore, it is possible for patients to make claims for these plans to reduce the amount of money expended in this field. People are not allowed to make insurance claims while they undergo cosmetic dental care like diamond implants, but extraction of teeth is always regarded as a necessity for patients; so most of the claims that are made in this front are settled easily.

It is still possible for them to pay less at the moment of the treatment, even if they have not opted for dental insurance policies. Some of the clinics offer plans which would allow patients to pay the tooth extraction cost in the form of installments. This is one of the better ways that people can consider if they are unable to pay the entire cost of tooth extraction immediately.

In fact, the cost of extracting one tooth is not very high and it is affordable to most people. Of course, if there are many other oral problems that you encounter, the extraction cost would be higher. Dentists would also consider the other problems you have and charge you additional fees accordingly. Not brushing the teeth regularly might aid in the development of plaque and this can make the cost of tooth extraction higher.

Maintaining a good oral health is important and it reflects the overall health of an individual.

To conclude, you need to know the information about cost of extraction so you can get the right service and must also follow certain easy practices to reduce the tooth extraction cost.

Source:http://ezinearticles.com/?Basic-Information-About-Tooth-Extraction-Cost&id=6623204

Importance of Data Mining Services in Business

Data mining is used in re-establishment of hidden information of the data of the algorithms. It helps to extract the useful information starting from the data, which can be useful to make practical interpretations for the decision making.

It can be technically defined as automated extraction of hidden information of great databases for the predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making. Although data mining is a relatively new term, the technology is not. It is thus also known as Knowledge discovery in databases since it grip searching for implied information in large databases.

It is primarily used today by companies with a strong customer focus - retail, financial, communication and marketing organizations. It is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. It is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.

However, the use of some advanced technologies makes it a decision making tool as well. It is used in market research, industry research and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, scientific tests, genetics, financial services and utilities.

Data mining consists of major elements:

•    Extract and load operation data onto the data store system.
•    Store and manage the data in a multidimensional database system.
•    Provide data access to business analysts and information technology professionals.
•    Analyze the data by application software.
•    Present the data in a useful format, such as a graph or table.

The use of data mining in business makes the data more related in application. There are several kinds of data mining: text mining, web mining, relational databases, graphic data mining, audio mining and video mining, which are all used in business intelligence applications. Data mining software is used to analyze consumer data and trends in banking as well as many other industries.

Source:http://ezinearticles.com/?Importance-of-Data-Mining-Services-in-Business&id=2601221

Scraping bids out for SS United States

Yesterday we posted that the Independence Seaport Museum doesn’t have the money to support the upkeep of the USS Olympia nor does it have the money to dredge the channel to tow her away.  On the other side of the river the USS New Jersey Battleship Museum is also having financial troubles. Given the current troubles centered around the Delaware River it almost seems a shame to report that the SS United States, which has been sitting of at Pier 84 in South Philadelphia for the last fourteen years,  is now being inspected by scrap dealers.  Then again, she is a rusting, gutted shell.  Perhaps it is time to let the old lady go.    As reported in Maritime Matters:

SS UNITED STATES For Scrap?

An urgent message was sent out today to the SS United States Conservancy alerting members that the fabled liner, currently laid up at Philadelphia, is being inspected by scrap merchants.

“Dear SS United States Conservancy Members and Supporters:

The SS United States Conservancy has learned that America’s national flagship, the SS United States, may soon be destroyed. The ship’s current owners, Genting Hong Kong (formerly Star Cruises Limited), through its subsidiary, Norwegian Cruise Line (NCL), are currently collecting bids from scrappers.

The ship’s current owners listed the vessel for sale in February, 2009. While NCL graciously offered the Conservancy first right of refusal on the vessel’s sale, the Conservancy has not been in a financial position to purchase the ship outright. However, the Conservancy has been working diligently to lay the groundwork for a public-private partnership to save and sustain the ship for generations to come.

Source:http://www.oldsaltblog.com/2010/03/scraping-bids-out-for-ss-united-states/

Scrape it – Save it – Get it

I imagine I’m talking to a load of developers. Which is odd seeing as I’m not a developer. In fact, I decided to lose my coding virginity by riding the ScraperWiki digger! I’m a journalist interested in data as a beat so all I need to do is scrape. All my programming will be done on ScraperWiki, as such this is the only coding home I know. So if you’re new to ScraperWiki and want to make the site a scraping home-away-from-home, here are the basics for scraping, saving and downloading your data:

With these three simple steps you can take advantage of what ScraperWiki has to offer – writing, running and debugging code in an easy to use editor; collaborative coding with chat and user viewing functions; a dashboard with all your scrapers in one place; examples, cheat sheets and documentation; a huge range of libraries at your disposal; a datastore with API callback; and email alerts to let you know when your scrapers break.

So give it a go and let us know what you think!

Source:https://blog.scraperwiki.com/2011/04/scrape-it-save-it-get-it/

Ethics in data journalism: mass data gathering – scraping, FOI and deception

Mass data gathering – scraping, FOI, deception and harm

The data journalism practice of ‘scraping’ – getting a computer to capture information from online sources – raises some ethical issues around deception and minimisation of harm. Some scrapers, for example, ‘pretend’ to be a particular web browser, or pace their scraping activity more slowly to avoid detection. But the deception is practised on another computer, not a human – so is it deception at all? And if the ‘victim’ is a computer, is there harm?

The tension here is between the ethics of virtue (“I do not deceive”) and teleological ethics (good or bad impact of actions). A scraper might include a small element of deception, but the act of scraping (as distinct from publishing the resulting information) harms no human. Most journalists can live with that.

The exception is where a scraper makes such excessive demands on a site that it impairs that site’s performance (because it is repetitively requesting so many pages in a small space of time). This not only negatively impacts on the experience of users of the site, but consequently the site’s publishers too (in many cases sites will block sources of heavy demand, breaking the scraper anyway).

Although the harm may be justified against a wider ‘public good’, it is unnecessary: a well designed scraper should not make such excessive demands, nor should it draw attention to itself by doing so. The person writing such a scraper should ensure that it does not run more often than is necessary, or that it runs more slowly to spread the demands on the site being scraped. Notably in this regard, ProPublica’s scraping project Upton “helps you be a good citizen [by avoiding] hitting the site you’re scraping with requests that are unnecessary because you’ve already downloaded a certain page” (Merrill, 2013).

Attempts to minimise that load can itself generate ethical concerns. The creator of seminal data journalism projects chicagocrime.org and Everyblock, Adrian Holovaty, addresses some of these in his series on ‘Sane data updates’ and urges being upfront about

    “which parts of the data might be out of date, how often it’s updated, which bits of the data are updated … and any other peculiarities about your process … Any application that repurposes data from another source has an obligation to explain how it gets the data … The more transparent you are about it, the better.” (Holovaty, 2013)

Publishing scraped data in full does raise legal issues around the copyright and database rights surrounding that information. The journalist should decide whether the story can be told accurately without publishing the full data.

Issues raised by scraping can also be applied to analogous methods using simple email technology, such as the mass-generation of Freedom of Information requests. Sending the same FOI request to dozens or hundreds of authorities results in a significant pressure on, and cost to, public authorities, so the public interest of the question must justify that, rather than its value as a story alone. Journalists must also check the information is not accessible through other means before embarking on a mass-email.

Source: http://onlinejournalismblog.com/2013/09/18/ethics-in-data-journalism-mass-data-gathering-scraping-foi-and-deception/

Finding & Removing Spam Blogs Who Scrape Content Onto Free Hosted Blogs

The more popular you become in the blogging world, the more crap you have to deal with!
Content scraping is one chore that can be dealt with swiftly once you understand what to do.
This post contains links which you can use to quickly and easily report content scrapers and spam blogs.
Please share this post and help clean up spam blogs and punish content scrapers.
First step is to find your url’s which have been scraped of content and then get the scrapers spam blog removed.

Some of the tools i use to do this are:

    Google Webmaster Tools
    Google Alerts

Finding Scraped Content
Login to your Google Webmaster Tools account and go to traffic > links to your site.
You should see something like this:
Webmaster Tools Links to Your Site

The first domain is a site which has copied and embedded my homepage which i have already dealt with.
The second site is a search engine.
The third domain is the one i want to deal with.

A common method scrapers use is to post the scraped content from your rss feed on to a free hosted blog like WordPress.com or blogger.com.

Once you click the WordPress.com link in webmaster tools, you’ll find all the url’s which have been scraped.
Links to Your Site

There’s 32 url’s which have been linked to so its simply a matter of clicking each of your links and finding the culprits.

The first link is my homepage which has been linked to by legit domains like WordPress developers.
The others are mainly linked to by spam blogs who have scraped the content and used a free hosted service which in this case is WordPress.com.
WordPress.com Links to Your Site
 Reporting & Removing Spam Blogs

Once you have the url’s of the content scraping blogs as seen in the screenshot above:

    Fill in this basic form to report spam to WordPress.com
    Fill in this form to report copyright content to WordPress.com
    Use this form to report Blogspot and Blogger.com content which has been scraped.
    Fill in one of these forms to remove content from Google

Google Alerts

Its very easy to setup a Google alert to find your post titles when they get scraped.
If you’ve setup the WordPress SEO plugin correctly, you should have included your site title at the end of all your post titles.
Then all you need to do is setup a Google alert for your site title and you’ll be notified every time a scraper links to your content.

Link Notifications

You may also receive a pingback or trackback if you have this feature enabled in your discussion settings.

Link Notifications
RSS Feed Links

Most content scrapers use automated software to scrape the content from RSS feeds.
Make sure you configure your Reading settings so only a summary is displayed.
Reading Settings Feed Summary

Next step is to configure the settings in Yoast’s SEO plugin so links back to your site are included in all RSS feed post summaries.

RSS Feed Links

This will help search engines identify you and your domain as the original author of the content.
There’s other services like copyscape and dmca which can help you protect your sites content if you’re prepared to pay a premium.
That’s it folks.
Its easy to find and get spam sites removed once you know what to do.
Hope you don’t have to deal with this garbage to often.
Ever found out your content has been scraped?
What did you do about it?

Source: http://wpsites.net/blogging/content-scraping-monitoring-and-prevention-tips/

Web Data Extraction / Scraping Data from Kitco Inc. Text Only Market Page

I wish to capture data from

<html>
<head>
<title>Text Only Market Page</title>
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
</head>

<body bgcolor="#FFFFFF">
<br><br>
<pre>
<b><font size=6>
  Kitco Inc.

  Text Only Market Page</font></b>

    <a href="http://www.kitco.com/market/">Graphic version of this page</a>

    <a href="http://www.kitco.com/market/LFrate.html">Precious Metals Lease Rates</a> 
    <a href="http://www.kitco.com/gold.londonfix.html">Historical Price Data</a> 
    <a href="http://www.kitco.com/market/marketnews.html">Precious Metals News Headlines</a>

    <font size=4><b><a href="https://online.kitco.com/bullion/completelist_USD.html#gold">Buy gold and silver online direct from Kitco!</a>
   Live quotes for all bullion products.</b></font>


   --------------------------------------------------------------------------------
   London Fix          GOLD          SILVER       PLATINUM           PALLADIUM
                   AM       PM                  AM       PM         AM       PM
   --------------------------------------------------------------------------------
   Jun 19,2012   1628.50   1625.50   28.8100   1486.00   1486.00   629.00   634.00 
   Jun 18,2012   1623.50   1615.50   28.4300   1486.00   1484.00   626.00   628.00 
   --------------------------------------------------------------------------------


                  New York Spot Price
                MARKET IS OPEN
            Will close in 4 hour 25 minutes
   ----------------------------------------------------------------------
   Metals          Bid        Ask           Change        Low       High
   ----------------------------------------------------------------------
   Gold         1619.80     1620.80     -8.90  -0.55%    1616.60  1632.70
   Silver         28.46       28.56     -0.28  -0.97%      28.24    28.95
   Platinum     1479.00     1489.00      0.00   0.00%    1476.00  1500.00
   Palladium     627.00      632.00      0.00   0.00%     622.00   639.00
   ----------------------------------------------------------------------
   Last Update on Jun 19, 2012 at 12:50.59
   ----------------------------------------------------------------------


                Asia / Europe Spot Price
                MARKET IS OPEN
            Will close in 4 hours 25 minutes
   ----------------------------------------------------------------------
   Metals                      Bid          Ask      Change from NY close
   ----------------------------------------------------------------------
   Gold                      1619.80      1620.80     -8.90   -0.55%
   Silver                      28.46        28.56     -0.28   -0.97%
   Platinum                  1479.00      1489.00     +0.00   +0.00%
   Palladium                  627.00       632.00     +0.00   +0.00%
   ----------------------------------------------------------------------
   Last Update on Jun 19, 2012 at 12:50.59
   ----------------------------------------------------------------------


<b>   File created on Tue Jun 19 12:51:04 2012</b>


        <style type="text/css"><!--
 #main_container_footer {width:100%;text-align: center;}
    #main_container_footer #footer_container {width:auto; margin:25px auto 25px auto;}
    #main_container_footer #footer_container ul {margin:0; padding:0;}
    #main_container_footer #footer_container ul li {float:left; display:inline; list-style:none; padding:0 8px; font-family:Verdana, Arial, Helvetica, sans-serif; font-size:12px; color:#000; border-right:1px #000 solid;}
    #main_container_footer #footer_container ul li a {font-family:Verdana, Arial, Helvetica, sans-serif; font-size:12px; color:#000; text-decoration:underline; font-weight:normal;}
    #main_container_footer #footer_container ul li a:hover {color:#ac1a2f; text-decoration:none; font-weight:normal;}
    #main_container_footer #footer_container ul li.no_border {border:0px;}
--></style>
  <table border="0" cellspacing="0" cellpadding="0"><tr><td>
 <div id="main_container_footer">
        <div id="footer_container">
            <ul>
                <li class="no_border"><script type="text/javascript">
copyright=new Date();
update=copyright.getFullYear();
document.write("&copy; "+ update + " Kitco Metals Inc.");
</script></li>
                <li><a href="https://corp.kitco.com/index.html">About Us</a></li>
                <li><a href="http://www.kitco.com/TermsofUse/" target="_top" onclick="Window_open(this.href,'KITCO','top=120,left=250,width=500,height=350'); return false">Website Terms of Use</a></li>
                <li><a href="https://online.kitco.com/help/privacy_policy.html" target="_top" onclick="Window_open(this.href,'KITCO','top=120,left=250,width=500,height=350'); return false">Privacy Policy</a></li>
                <li><a href="http://www.kitco.com/ads/">Advertise With Us</a></li>
                <li><a href="https://corp.kitco.com/en/corporate_culture.html">Careers</a></li>
                <li><a href="https://corp.kitco.com/en/contact.html" target="_top" onclick="Window_open(this.href,'KITCO','top=120,left=250,width=500,height=350'); return false">Contact Us</a></li>
                <li class="no_border"><a href="https://corp.kitco.com/en/feedback.html" target="_top" onclick="Window_open(this.href,'KITCO','top=120,left=250,width=500,height=350'); return false">Feedback</a></li>
            </ul>
        </div>
    </div> 

    </td></tr></table><br /><br />
<script language="JavaScript" type="text/javascript">
<!--
function Window_open (Address) {
  NewWindow = window.open(Address, "Popup", "width=695,height=600,left=100,top=200,resizable=yes,scrollbars=yes");
  NewWindow.focus();
}
// -->
</script>
 <!-- img src="http://www.kitco.com/scripts/counter/counter.pl?txtonlyE.txt" width="1" height="1" -->
<!-- Google-Analytics Code-->
<script type="text/javascript">
  var _gaq = _gaq || [];
  _gaq.push(['_setAccount', 'UA-4074364-3']);
  _gaq.push(['_trackPageview']);

  (function() {
    var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
    ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
    var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
  })();
</script>
</body>
</html>

More specifically, I am looking to capture the following data:

--------------------------------------------------------------------------------
London Fix          GOLD          SILVER       PLATINUM           PALLADIUM
               AM       PM                  AM       PM         AM       PM
--------------------------------------------------------------------------------
Jun 19,2012   1628.50   NA        28.8100   1486.00   1486.00   629.00   634.00 
Jun 18,2012   1623.50   1615.50   28.4300   1486.00   1484.00   626.00   628.00 
--------------------------------------------------------------------------------

Does anybody have any suggestions how I can do this using PHP?



1 Answer


Quick and dirty regex method:

$data = file_get_contents('http://www.kitco.com/texten/texten.html');
preg_match_all('/([A-Z]{3,5}\s+[0-9]{1,2},[0-9]{4}\s+([0-9.NA]{2,10}\s+){1,7})/si',$data,$result);

$records = array();
foreach($result[1] as $date) {
    $temp = preg_split('/\s+/',$date);
    $index = array_shift($temp);
    $index.= array_shift($temp);
    $records[$index] = implode(',',$temp);
}
print_R($records);

Note, you'd probably want to add some validation, etc.


Source: http://stackoverflow.com/questions/11103001/web-data-extraction-scraping-data-from-kitco-inc-text-only-market-page

Scraping webdata from a website that loads data in a streaming fashion

I'm trying to scrape some data off of the FEC.gov website using python for a project of mine. Normally I use python

mechanize and beautifulsoup to do the scraping.

I've been able to figure out most of the issues but can't seem to get around a problem. It seems like the data is

streamed into the table and mechanize.Browser() just stops listening.

So here's the issue: If you visit http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A ... you get the first 500

contributors whose last name starts with A and have given money to candidate P80003338 ... however, if you use

browser.open() at that url all you get is the first ~5 rows.

I'm guessing its because mechanize isn't letting the page fully load before the .read() is executed. I tried putting a

time.sleep(10) between the .open() and .read() but that didn't make much difference.

And I checked, there's no javascript or AJAX in the website (or at least none are visible when you use the 'view-

source'). SO I don't think its a javascript issue.

Any thoughts or suggestions? I could use selenium or something similar but that's something that I'm trying to avoid.

-Will

2 Answers

Why not use an html parser like lxml with xpath expressions.

I tried

>>> import lxml.html as lh
>>> data = lh.parse('http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A')
>>> name = data.xpath('/html/body/table[2]/tr[5]/td[1]/a/text()')
>>> name
[' AABY, TRYGVE']
>>> name = data.xpath('//table[2]/*/td[1]/a/text()')
>>> len(name)
500
>>> name[499]
' AHMED, ASHFAQ'
>>>



Similarly, you can create xpath expression of your choice to work with.


Source: http://stackoverflow.com/questions/9435512/scraping-webdata-from-a-website-that-loads-data-in-a-streaming-

fashion

How can I circumvent page view limits when scraping web data using Python?

I am using Python to scrape US postal code population data from http:/www.city-data.com, through this directory: http://www.city-data.com/zipDir.html. The specific pages I am trying to scrape are individual postal code pages with URLs like this: http://www.city-data.com/zips/01001.html. All of the individual zip code pages I need to access have this same URL Format, so my script simply does the following for postal_code in range:

    Creates URL given postal code
    Tries to get response from URL
    If (2), Check the HTTP of that URL
    If HTTP is 200, retrieves the HTML and scrapes the data into a list
    If HTTP is not 200, pass and count error (not a valid postal code/URL)
    If no response from URL because of error, pass that postal code and count error
    At end of script, print counter variables and timestamp

The problem is that I run the script and it works fine for ~500 postal codes, then suddenly stops working and returns repeated timeout errors. My suspicion is that the site's server is limiting the page views coming from my IP address, preventing me from completing the amount of scraping that I need to do (all 100,000 potential postal codes).

My question is as follows: Is there a way to confuse the site's server, for example using a proxy of some kind, so that it will not limit my page views and I can scrape all of the data I need?

Thanks for the help! Here is the code:

##POSTAL CODE POPULATION SCRAPER##

import requests

import re

import datetime

def zip_population_scrape():

    """
    This script will scrape population data for postal codes in range
    from city-data.com.
    """
    postal_code_data = [['zip','population']] #list for storing scraped data

    #Counters for keeping track:
    total_scraped = 0
    total_invalid = 0
    errors = 0


    for postal_code in range(1001,5000):

        #This if statement is necessary because the postal code can't start
        #with 0 in order for the for statement to interate successfully
        if postal_code <10000:
            postal_code_string = str(0)+str(postal_code)
        else:
            postal_code_string = str(postal_code)

        #all postal code URLs have the same format on this site
        url = 'http://www.city-data.com/zips/' + postal_code_string + '.html'

        #try to get current URL
        try:
            response = requests.get(url, timeout = 5)
            http = response.status_code

            #print current for logging purposes
            print url +" - HTTP:  " + str(http)

            #if valid webpage:
            if http == 200:

                #save html as text
                html = response.text

                #extra print statement for status updates
                print "HTML ready"

                #try to find two substrings in HTML text
                #add the substring in between them to list w/ postal code
                try:           

                    found = re.search('population in 2011:</b> (.*)<br>', html).group(1)

                    #add to # scraped counter
                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])

                    #print statement for logging
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."
                #if substrings not found, try searching for others
                #and doing the same as above   
                except AttributeError:
                    found = re.search('population in 2010:</b> (.*)<br>', html).group(1)

                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."

            #if http =404, zip is not valid. Add to counter and print log        
            elif http == 404:
                total_invalid +=1

                print postal_code_string + ": Not a valid zip code. " + str(total_invalid) + " total invalid zips."

            #other http codes: add to error counter and print log
            else:
                errors +=1

                print postal_code_string + ": HTTP Code Error. " + str(errors) + " total errors."

        #if get url fails by connnection error, add to error count & pass
        except requests.exceptions.ConnectionError:
            errors +=1
            print postal_code_string + ": Connection Error. " + str(errors) + " total errors."
            pass

        #if get url fails by timeout error, add to error count & pass
        except requests.exceptions.Timeout:
            errors +=1
            print postal_code_string + ": Timeout Error. " + str(errors) + " total errors."
            pass


    #print final log/counter data, along with timestamp finished
    now= datetime.datetime.now()
    print now.strftime("%Y-%m-%d %H:%M")
    print str(total_scraped) + " total zips scraped."
    print str(total_invalid) + " total unavailable zips."
    print str(errors) + " total errors."



Source: http://stackoverflow.com/questions/25452798/how-can-i-circumvent-page-view-limits-when-scraping-web-data-using-python

Web data scraping (online news comments) with Scrapy (Python)

Since you seem like the try-first ask-question later type (that's a very good thing), I won't give you an answer, but a

(very detailed) guide on how to find the answer.

The thing is, unless you are a yahoo developer, you probably don't have access to the source code you're trying to

scrape. That is to say, you don't know exactly how the site is built and how your requests to it as a user are being

processed on the server-side. You can, however, investigate the client-side and try to emulate it. I like using Chrome

Developer Tools for this, but you can use others such as FF firebug.

So first off we need to figure out what's going on. So the way it works, is you click on the 'show comments' it loads

the first ten, then you need to keep clicking for the next ten comments each time. Notice, however, that all this

clicking isn't taking you to a different link, but lively fetches the comments, which is a very neat UI but for our

case requires a bit more work. I can tell two things right away:

    They're using javascript to load the comments (because I'm staying on the same page).
    They load them dynamically with AJAX calls each time you click (meaning instead of loading the comments with the

page and just showing them to you, with each click it does another request to the database).

Now let's right-click and inspect element on that button. It's actually just a simple span with text:

<span>View Comments (2077)</span>

By looking at that we still don't know how that's generated or what it does when clicked. Fine. Now, keeping the

devtools window open, let's click on it. This opened up the first ten. But in fact, a request was being made for us to

fetch them. A request that chrome devtools recorded. We look in the network tab of the devtools and see a lot of

confusing data. Wait, here's one that makes sense:

http://news.yahoo.com/_xhr/contentcomments/get_comments/?content_id=42f7f6e0-7bae-33d3-aa1d-

3dfc7fb5cdfc&_device=full&count=10&sortBy=highestRated&isNext=true&offset=20&pageNumber=2&_media.modules.content_commen

ts.switches._enable_view_others=1&_media.modules.content_comments.switches._enable_mutecommenter=1&enable_collapsed_com

ment=1

See? _xhr and then get_comments. That makes a lot of sense. Going to that link in the browser gave me a JSON object

(looks like a python dictionary) containing all the ten comments which that request fetched. Now that's the request you

need to emulate, because that's the one that gives you what you want. First let's translate this to some normal reqest

that a human can read:

go to this url: http://news.yahoo.com/_xhr/contentcomments/get_comments/
include these parameters: {'_device': 'full',
          '_media.modules.content_comments.switches._enable_mutecommenter': '1',
          '_media.modules.content_comments.switches._enable_view_others': '1',
          'content_id': '42f7f6e0-7bae-33d3-aa1d-3dfc7fb5cdfc',
          'count': '10',
          'enable_collapsed_comment': '1',
          'isNext': 'true',
          'offset': '20',
          'pageNumber': '2',
          'sortBy': 'highestRated'}

Now it's just a matter of trial-and-error. However, a few things to note here:

    Obviously the count is what decides how many comments you're getting. I tried changing it to 100 to see what

happens and got a bad request. And it was nice enough to tell me why - "Offset should be multiple of total rows". So

now we understand how to use offset

    The content_id is probably something that identifies the article you are reading. Meaning you need to fetch that

from the original page somehow. Try digging around a little, you'll find it.

    Also, you obviously don't want to fetch 10 comments at a time, so it's probably a good idea to find a way to fetch

the number of total comments somehow (either find out how the page gets it, or just fetch it from within the article

itself)

    Using the devtools you have access to all client-side scripts. So by digging you can find that that link to

/get_comments/ is kept within a javascript object named YUI. You can then try to understand how it is making the

request, and try to emulate that (though you can probably figure it out yourself)

    You might need to overcome some security measures. For example, you might need a session-key from the original

article before you can access the comments. This is used to prevent direct access to some parts of the sites. I won't

trouble you with the details, because it doesn't seem like a problem in this case, but you do need to be aware of it in

case it shows up.

    Finally, you'll have to parse the JSON object (python has excellent built-in tools for that) and then parse the

html comments you are getting (for which you might want to check out BeautifulSoup).

As you can see, this will require some work, but despite all I've written, it's not an extremely complicated task

either.

So don't panic.

It's just a matter of digging and digging until you find gold (also, having some basic WEB knowledge doesn't hurt).

Then, if you face a roadblock and really can't go any further, come back here to SO, and ask again. Someone will help

you.


Source: http://stackoverflow.com/questions/20218855/web-data-scraping-online-news-comments-with-scrapy-python

A good web data extraction/screen scraper program?

I need to capture product data from a site on a regular basis and wondered if any one knows of a good software program? I've trialed Mozenda but its a monthly subscription and pricey in the long term. Obviously something thats free would be best but I don't mind paying either. Just need a decent program thats reliable and doesn't require much programming knowledge.

You can try ScraperWiki.com if you know python.

I've experimented with Screen-Scraper and found it easy to use. The application comes in multiple versions: basic (which is free), professional, and enterprise. Also, multiple platforms are supported.

Hire a programmer to do it so that there is only a one off cost. I often see similar projects on freelancing websites like Elance and oDesk.

I really like iMacros. You can give it a test drive to see if it meets your needs with the totally free Firefox extension (there's also IE versions), but there are also more full featured application and "server" versions that have more features and ability to do thing in an unattended manner.

Here are some other alternatives to consider:

    License the data from the provider. Call em up and ask 'em.

    Use Amazon Mechanical Turk to get humans to copy and paste and format it for ya. They are cheap.

    For automation, it depends on how complicated the HTML is and how often it changes. You could use Excel's Web Data Import if it's really simple.


You can use irobot from IRobotSoft, which is totally free, and provides more functionalityies than other paid software. Watch demos here http://irobotsoft.com/help/ for how simple it is.

Questions on their forum were answered very quickly.


Source: http://stackoverflow.com/questions/2334164/a-good-web-data-extraction-screen-scraper-program

How to login to website and extract data using PHP [closed]

I have installed the tiny tiny rss on to my computer (Windows) and also have Xampp installed (localhost).

I want to be able to use PHP to extract data from the Tiny tiny RSS webpage.

I have tried this it which just opens the front page:

<?php
$homepage = file_get_contents('my install tiny tiny rss url');
echo $homepage;
?>

But how do I login and extract the data.

You can use cURL to send post data and headers. To login you need to replicate the exact data exchange between the client and the server.


SOurce: http://stackoverflow.com/questions/20611918/how-to-login-to-website-and-extract-data-using-php

Is it ok to scrape data from Google results?

I'd like to fetch results from Google using curl to detect potential duplicate content. Is there a high risk of being banned by Google?

Google will eventually block your IP when you exceed a certain amount of requests.



Google disallows automated access in their TOS, so if you accept their terms you would break them.

That said, I know of no lawsuit from Google against a scraper. Even Microsoft scraped Google, they powered their search engine Bing with it. They got caught in 2011 red handed :)

There are two options to scrape Google results:

1) Use their API

    You can issue around 40 requests per hour You are limited to what they give you, it's not really useful if you want to track ranking positions or what a real user would see. That's something you are not allowed to gather.

    If you want a higher amount of API requests you need to pay.
    60 requests per hour cost 2000 USD per year, more queries require a custom deal.

2) Scrape the normal result pages

    Here comes the tricky part. It is possible to scrape the normal result pages. Google does not allow it.
    If you scrape at a rate higher than 15 keyword requests per hour you risk detection, higher than 20/h will get you blocked from my experience.
    By using multiple IPs you can up the rate, so with 100 IP addresses you can scrape up to 2000 requests per hour. (50k a day)
    There is an open source search engine scraper written in PHP at http://scraping.compunect.com It allows to reliable scrape Google, parses the results properly and manages IP addresses, delays, etc. So if you can use PHP it's a nice kickstart, otherwise the code will still be useful to learn how it is done.


Source: http://stackoverflow.com/questions/22657548/is-it-ok-to-scrape-data-from-google-results

Data Scraping from PDF and Excel

I am doing a little data scraping, There are 3 types of file from which i am scraping data.

1- HTML
2- PDF
3- Excel(xls)

For HTML i am comfortable, i am using HTML Agility for that.

For PDF and excel i need suggestions from anyone.



Concerning Excel. If you are in a MS environment you can either do Office Automation or use OLEDB. In a Java

environment look at Apache POI.

EDIT: Concerning PDF in Java try Apache PDFBox . Can also work in .NET using IKVM

I can recommend Cogniview's PDF2XL, a reasonably inexpensive commercial product, to extract data from tables in PDF

files into Excel. We have used it with great success.

HTML Agility is a library. Its good to use. But then, why do you need separate tools for different data extraction

purposes? Use Automation Anywhere to extract data from any source. As far as I know, it would work for all the three

sources you have specified. Google it.

Source: http://stackoverflow.com/questions/3147803/data-scraping-from-pdf-and-excel

Excel VBA Data Mining Real-Time Data from a Web Page that Refreshes Data

I want to capture real-time data that updates into a table on a webpage; I prefer capturing it into excel using VBA, but I will write it in .NET C# or VB if I that is easier.

the data updates about 1 or 2 seconds, and I want to just grab the latest data quotes and log it into my spreadsheet; the table names are the same, only the data refreshes, and it does so automatically on the web page.

I've done a lot of Excel VBA and I know how to download a URL to a file--this is NOT what I want; I want to gain access to my webpage that is active and grab the data updates after I've logged into my site and selected a webpage that I like.

Is there a simple way to access this data on the webpage from Excel or .Net? Because it refreshes no more than once every 1 or 2 seconds, it is easy to just keep checking it for updates, and I can compare the latest data to see if it actually refreshed.


In Excel 2003, use Data/Import External Data/New Web Query
Browse to your page and select the table you want to import.
After that you can either do a manual Refresh, or use a timer procedure to do something like:

Source: http://stackoverflow.com/questions/9855794/excel-vba-data-mining-real-time-data-from-a-web-page-that-refreshes-data

Need to pull data from a website…web query? macro?

I have a list of every DOT # (Dept. of Trans.) in the country. I want to find out insurance effective date for each one of these companies. If you go to http://li-public.fmcsa.dot.gov --> "continue" --> then from the dropdown select "carrier search" and hit "go" it'll take you to a search form (that is the only way to get to this screen).

From there, you can input a DOT # X (use 61222 as an example) and it'll bring you to another screen. Click "view report in HTML" and then down on the bottom you'll see "Active/Pending Insurance". I want to pull the "effective date" from that page and stick it in the spreadsheet next to the DOT # X that I already know.

Of the thousands of DOT #'s in my list, not all will have filings on this website, if that makes a difference.

Can this be done with a Macro or Excel Web Query? I know I probably sound like a total novice, but I'd appreciate any help I could get.

Can you do it? Frankly even if you could you'd lock up the spreadsheet while it's doing that processing. And in the end, how would you handle an error half-way through?

I'd not do this in a client-facing application. This sounds more like something to do in server-side app that can do the processing and gather the information in a more controlled environment. Then you Excel spreadsheet could query that app and get the information in one fell swoop. Error handling is much simpler and you don't end up sitting there staring at Excel why it works its way through thousands of web sites. It was not built to do that elegantly.

What do you write the web service I'm describing in? Well it depends on your preference. Me, I'd write it in Ruby on Rails since it can easily handle the scraping aspect of the task and can report the data out easily as well. But it really falls back to whatever you're most comfortable coding in.


Source: http://stackoverflow.com/questions/15286429/need-to-pull-data-from-a-website-web-query-macro

How to extract data from web 2.0 graphs using a scraper

I have recently come across a web page containing a graph object that displays the (x, y) values on the object as the

mouse is rolled across it. Is there any way to automate the extraction of this data?

How is the graph data loaded? If embedded in the page source then you can extract it with xpath or regex. Else use

Firebug to see how it is loaded.



You will need a solution that works inside the web browser, so the AJAX/Javascript is properly rendered.

I have used iMacros with good success for web scraping in the past. There are free/open-source and "PRO" paid editions

(comparison table here).

Another option is always to custom code something with the Microsoft webbrowser control.


Source: http://stackoverflow.com/questions/3980774/how-to-extract-data-from-web-2-0-graphs-using-a-scraper

Legality of Web Scraping vs Normal Use

I know the topic of web scraping has been discussed before (example), and I understand it's a bit of a grey area depending on a lot of factors (e.g. website's terms of use).

What I'd like to ask is: how is web scraping any different from (a) how we access the webpage via a web browser, and (b) how web crawlers (e.g. Google) download and index webpages?

Without knowing the legal background, I can't help but think that they're all just HTTP requests. If web scraping is illegal, then so should crawling and indexing (for instance be illegal).

Of course if your program is hitting the server so hard that it causes a denial of service, it's a different story altogether... my point is simply accessing and using data that is already open to the public.



I know this is a dead thread, but it would be nice to place some legal implications here due to its ranking in my Google Search. I cannot help but figure I am not the only one who searches like I do.

Legally, in the US, there are a few factors that seem to be important.

    Are you doing anything that is akin to hacking or gaining unauthorized access via the Computer Fraud and Abuse Act. Exploiting vulnerabilities and passing SQL in the URL to open a database no matter how bad the idiot programming like that was is illegal with a 15 year sentence (see the cases where an individual exploited security vulnerabilities in Verizon). Also, add a time out even if you round robin or use proxies. DDoS attacks are attacks. 1000 requests per second can shut down a lot of servers providing public information. The result here is up to 15 years in jail.

    Copyright Law: As mentioned, pure replication of data is illegal. Even 4% replication has been deemed a breach. With the recent gutting of the DMCA, a person is even more vulnerable to civil and criminal penalties.

    Trespass and Chattels: The following from wikipedia says it all.

    U.S. courts have acknowledged that users of "scrapers" or "robots" may be held liable for committing trespass to chattels,[5][6] which involves a computer system itself being considered personal property upon which the user of a scraper is trespassing. The best known of these cases, eBay v. Bidder's Edge, resulted in an injunction ordering Bidder's Edge to stop accessing, collecting, and indexing auctions from the eBay web site.

    Paywalls and Product: When going behind paywalls and breaching contract by clicking an agreement not to do something and then doing it, you add fuel to the protection of negligence v. willingness [an issue for damages and penalties not guilt] in civil and any criminal trials. (sorry originally wanted to say ignorance but it really isn't a defense)

    International: EU law and other law is way more lax. Corporations with big budgets dominate our legal landscape. They control the system in a very real way with their $$$.

Basically, get public information and information that is available without going behind a pay wall. Think like a user of the internet and combine a bunch of sources into a unique product. Don't just 'steal' an entire site (it isn't really stealing if it is a government site that offers public data especially for download but is if you download all or even more than a couple of the listings on ebay). Read the terms and conditions to know who actually owns the content.

Here are a few examples. Trulia owns its information but you could use it to go to an agents website and collect a legal amount of information. The legal amount is determinable. However, a public MLS listing lookup site with no agreement or terms and offering data to the public is fair game. The MLS numbers lists, however, are normally not fair game.

If a researcher can get to data, so can you. If a researcher needs permission, so do you. A computer is like having a million corporate researchers at your disposal.

AS for company policy, it is usually used internally to shield from liability and serves as a warning but is not entirely enforceable. The legal parts letting you know about copyrights and such are and usually are supposed to be known by everyone. Complete ignorance is not a legal protection. It does provide a ground set of rules. Be nice, or get banned is that message as far as I know.

My personal strategy is to start with public data and embellish it within legal means.


Source: http://stackoverflow.com/questions/14735791/legality-of-web-scraping-vs-normal-use

Anyone knows an online tool that can scrape a page and create a REST API for the scraped data?

I'm looking for a SaaS solution that is able to login to a platform, scrape data (reports) and then allow accessing the data through an API. I have some reporting platforms that provide web reporting and email reporting but with no API. Online reporting doesn't help and email reporting, although can be automated and scraped, isn't so reliable.

If you are willing to do the scraping through your own connection, have a look at Import IO. They have a desktop application that you use to teach the system how to scrape a page, and then you run the crawler from that application - and you can run it for as long as you like, as far as I can tell.

You may then upload your data to the Import cloud, from where it is available via an API on the import.io servers. Useful data can be made public to donate it "to the commons" if you wish.


I did some more digging, found iMacros as a possible solution. Its Windows based, which is a drawback in my case, but it does allow automation of the scraping and afterwards interaction via common web scripting languages like PHP and ASP.net.


If you are familiar with jQuery, I think you can use node.js and Cheerio module, then you can create a simple application to do auto scraping. Actually I have already built a site to do on line web scraping based on the above mentioned tech, the site is www.datafiddle.net, you can take a look at it.


Source: http://stackoverflow.com/questions/19646028/anyone-knows-an-online-tool-that-can-scrape-a-page-and-create-a-rest-api-for-the

Extract data from Web Scraping C#

I am MVC ASP.NET developer.

I have received the contents from any url, i.e. http, https etc. using WebRequest class.

I have received all the content of that particular url. (for now I took http://google.com)

My next step is to extract buttons, header, footer, colors, text etc.

Here is my code for now:

public ActionResult GetContent(UrlModel model) //model having a string URL
which is entered in a text box and method hits using submit button.
{
    //WebRequest request = WebRequest.Create(model.URL);

    WebRequest request = WebRequest.Create(model.URL);

    request.Credentials = CredentialCache.DefaultCredentials;

    WebResponse response = request.GetResponse();

    Stream dataStream = response.GetResponseStream();

    StreamReader reader = new StreamReader(dataStream);

    string responseFromServer = reader.ReadToEnd();
    ViewBag.Response = responseFromServer;

    reader.Close();
    response.Close();
    return View();
}

Can someone help me with writing the code ?

Also do suggest me with some techniques of data extraction in C#.



Source: http://stackoverflow.com/questions/21901162/extract-data-from-web-scraping-c-sharp