ࡱ> @ {jbjbܡܡ tOl:::::::FFFF8~ <F    S U U U U U U ,  : ::  R: : S N||::::S h :: ,9΍FFqF  4   Electronic Text Meets High-Performance Computing: The Cornell Web Lab William Y. Arms Cornell University Revised: February 15, 2007 Very large collections of electronic texts Exciting things happen when electronic text meets high performance computing. Very large collections provide opportunities for new types of research, but the scale creates new sets of challenges. While a scholar can read only one document a supercomputer can read millions simultaneously, but a person has a rich understanding of what is being read while a computer can analyze content at only a very superficial level. This paper explores these themes using a particular collection as a case study, the Cornell Web Laboratory (Arms 2006a). The details describe this collection, but the underlying concepts apply to any very large digital library. To start, we need a definition of the term "high-performance computing", recognizing that the capabilities of computing systems increase every year. An informal definition is that the collections of data and the computations on them are so vast that considerations of scale influence every decision about what research can be done and how to do it. For textual materials, the current limit is measured in billions of pages. In computing terms, the largest digital library collections range from 100 terabytes to 1,000 terabytes, or one petabyte, i.e., 1,000,000,000,000,000 bytes. Here are some examples: The Internet Archive's historical collection of the Web has more than 60 billion Web pages, which is about 700 terabytes of heavily compressed data (http://www.archive.org/). This collection is the subject of this case study. The Library of Congress's National Digital Information Infrastructure and Preservation Program (NDIIPP) supports collaborative projects to preserve digital content for the long term (http://www.digitalpreservation.gov/). There is no published estimate for the aggregate size of these projects, but a rough estimate is about one petabyte. The USC Shoah Foundation's video collection of interviews with survivors of genocide and other oppressions, notably the Nazi holocaust, is about 400 terabytes (http://www.usc.edu/schools/college/vhi/). The Open Content Alliance, Google, Microsoft, Yahoo, and others have large-scale programs to digitize printed books held in research libraries. Nobody has published an estimate of how large these collections will be but the contribution of even a single university, such as Cornell, will eventually be several petabytes of online data. As these examples illustrate, these are not fringe collections; they will be of mainstream importance to humanities and the social sciences. Soon, a large proportion of the world's library collections will be in digital form. How will scholars use these huge collections? How will they harness high-performance computing for research on such large of bodies of text and other digital materials? The size of these collections brings an unexpected benefit. They are so big that they are of interest to supercomputing specialists for the technical challenges that they bring. In the United States, the National Science Foundation (NSF) funds supercomputing centers for computing that is beyond the capabilities of individual universities. Modern supercomputing has evolved primarily to serve the natural sciences and engineering. In the past few years, however, very large text collections have become an appealing subject for research, thus bringing new expertise and new sources of funding to the humanities and social sciences. The first NSF grant toward building the Web Lab at Cornell was in partnership with astronomers from the Arecibo radio telescope in Puerto Rico. Although the content could not be more different, the computing problems are similar, with comparable amount of data and similar problems in organizing it. When the collections get large, only the computer reads every word In a recent seminar, Greg Crane of Tufts University made the simple but profound statement, "When collections get large, only the computer reads every word." In a traditional library the scholar personally browses the collection, searches through the catalog, and takes books off the shelves. With very large digital collections, the equivalent functions are performed by computer programs, acting as agents for people. Researchers do not interact with the collections directly. They use computer programs to extract small parts of the collection and rarely view individual items except after preliminary screening by programs. These very large collections require a new generation of automated computing tools that will analyze text for researchers. Our work at Cornell combines aspects of a large digital library with the development of new tools for researchers. We call it a laboratory, rather than a digital library, because the tools are designed to serve specific categories of research. Before describing the laboratory, we need to explain why methods for managing and analyzing textual materials that are excellent for moderately large digital libraries fail with very large collections. Traditional methods of organizing scholarly collections use metadata created by people. Cataloguing by skilled bibliographers has been central to libraries. In recent years, such metadata has been greatly augmented by mark-up embedded within the text itself. The amount of metadata and the number of texts with scholarly mark-up is impressive, but there is a human limit on what that can be generated manually. The biggest library catalogue, at OCLC, has tens of million of records created manually. The Internet Archive's Web collection has tens of billions of Web pages. They cannot be catalogued manually. There is also a technical limit on the rich data structures that can be manipulated in even the most powerful modern computer. Semantic Web techniques, such as RDF, have great flexibility for representing rich intellectual structures and have been incorporated in elegant systems for managing complex library objects. Perhaps the most highly developed is Fedora (http://www.fedora.info/). But the flexibility requires so much computing power that these systems are unable to manage vast amounts of data. As yet, the biggest Fedora repository is a few million records. The capabilities of such repositories will increase steadily with time, but they are a long way from the scale of the very large collections discussed in this paper. Recently, there has been interesting research in semi-automated methods for creating metadata and tagging electronic texts. These methods use techniques from machine learning and natural language processing, supplemented by human expertise. The name-recognition system developed for the Perseus Digital Library is a good example (Crane 2006). It uses a substantial computer system, a sixty-four node Linux cluster. Another example comes from work on expert-guided Web crawling, where an expert sets up the parameters for a focused Web crawl. Automated methods based on machine learning techniques do much of the work, but they succeed only because of skilled human intervention. Infomine/iVia at the University of California, Riverside is probably the leader in this work (Mitchell 2003). These semi-automated systems can analyze and organize hundreds of millions of records, but they are still small by the standards of emerging digital libraries. The Internet Archive Web collection The Cornell Web Lab (http://www.infosci.cornell.edu/SIN/WebLab/) is based on the historical Web collection of the Internet Archive (http://www.archive.org/). Approximately every two months since 1996, Brewster Kahle and his colleagues at the Internet Archive have collected a snapshot of the Web and preserved it for future generations. This is a remarkable resource. It is difficult to imagine how much of the history of our times would have been lost but for the Internet Archive. The early materials in particular are a unique record of the early days of the Web. Here are some rough statistics of the size of the collection. The total archive is about 60,000,000,000 pages (60 billion). Recent crawls are about 40-60 terabytes (compressed). The total archive is about 700 terabytes (compressed). Uncompressed, the archive would be about 7 petabytes. The rate of increase is about 1 terabyte/day (compressed). The collection is not perfect. There are gaps are because not all formats were collected in the early days. Some materials have been lost because of technical problems; the Internet Archive could not afford to duplicate everything. But the main problem is copyright. If the managers of a Web site do not want their materials copied, the Internet Archive does not collect it. Examples of organizations that block archiving of their collections include most newspapers and state governments in the United States. The President's Web site, whitehouse.gov, has a robots.txt notice disallowing collection. Even Google, which makes its business crawling other sites, does not allow its own site to be crawled. Some of us believe that the copyright law should permit or even encourage national libraries the Library of Congress in the US to collect and preserve all Web materials in the public interest, but the Internet Archive has to observe the law as it is currently understood. The Internet Archive provides public access to the collection through a computing system called the Wayback Machine (http://www.archive.org/). To use the Wayback Machine, a user types a URL into a search box. The system returns a list of all the dates for which it holds a page retrieved from that URL. If the user clicks on the date, the page is returned in as close to the original form as possible. This system is heavily used by people who wish to know about the state of the world at a given time. One of the interesting uses is in legal disputes. What information was available at a certain date? The research dialog The Wayback Machine is a wonderful system, but it only goes so far. It is organized for human retrieval of individual pages, not for computer programs that analyze millions of pages simultaneously. For several years, a group of us at Cornell have been interested in alternative ways to organize this data for novel types of research. Because the new styles of research are only partially understood, the design of the lab has been an iterative dialog between researchers and the computer specialists who are developing it. One of the stimuli was a chance remark by a computer scientist who was doing research on clustering algorithms on the Web. He mentioned that his team was spending 95 percent of its time obtaining test data, and 5 percent of the time actually doing research. This appears to be a common experience for people who do research on these large collections.  [Insert Figure 1 here.] Figure 1: The research dialog Figure 1 summarizes the design process that has emerged. The dialog begins with a group of researchers, mostly social scientists with some computer scientists. They have general ideas of studies they would like to do, based on concepts that are rooted in their disciplines. Unfortunately, those of us who are building the lab do not known how to create the services that would be ideal for the researchers. We do not know how to create the necessary data structures, algorithms, and computing systems. However we may be able to offer related services and often suggest alternative approaches to the research. These suggestions may in turn stimulate the researchers to consider new methods. Eventually, from this the dialog an experiment emerges that is interesting from the discipline point of view, and feasible from the computing point of view. The experiment is carried out, both parties learn more, and the process continues. To begin the dialog, in fall 2004, a group of students interviewed fifteen faculty and graduate students from social sciences and computer science to discover what research they would like to do on the history of the Web. From these interviews, we gained an understanding of the potential areas of research and the tools that are needed. One area is social science research on how the Web has developed. The Web is a social phenomenon of interest in its own right, which can be studied for topics such as the polarization of opinions or the impact on political campaigns. It also provides evidence of current social events, such as the spread of urban legends and the development of legal concepts across time. Social and information networks form an area of research that is of interest in many disciplines, including computer science and social sciences such as sociology, communication, and ethnography. For example, the tendency of people to come together and form groups is inherent in the structure of society; the ways in which such groups take shape and evolve over time is a common theme of social science research. In one experiment, computer scientists associated with the Web Lab analyzed a billion items of data from LiveJournal to analyze how the tendency of an individual to join a community is influenced not just by the number of friends within the community, but also by how those friends are connected to each other (Backstrom 2006). In related research, sociologists are exploring a new methodology for exploring the diffusion of ideas. The traditional methodology for such research is to carry out a retrospective survey. The data is coded by hand for detailed analysis. The new methodology is to mine huge numbers of Web pages automatically for contemporary evidence. Another research area is the structure and evolution of the Web itself. In recent years a series of classic experiments have analyzed the structure of the Web, but these experiments require so much data that many of them have been done only once or at most repeated a few times, and rarely at different dates in the evolution of the Web. It is difficult to distinguish fundamental characteristics of the Web from properties of how the test data was gathered, or to observe how these properties are changing with time. A final research area that was identified by the interviews is focused Web crawling. The particular question comes from work in educational digital libraries (Bergmark 2002). Can the selection of materials for a library be automated? For example, suppose that somebody is teaching a course on sea borne trade in classical times. Is it possible to select automatically a reading list of materials from the Web suitable for a university class on this topic? Building the Web Lab The Cornell Web Lab supports such research by mounting many snapshots of the Web from the past decade and providing tools and facilities for research. The process is to copy snapshots of the Web from the Internet Archive; index the snapshots and store them online at Cornell; extract features that are useful for researchers; build a general purpose set of tools; provide a Web interface for less technical uses and an application program interface (API) for technically sophisticated researchers.  [Insert Figure 2 here.] Figure 2. The Web Lab system Figure 2 is a diagram of the current computer system. An earlier version is described in (Arms 2006b). Few scholars would recognize this diagram as a library, but it may be more representative of the library of the future than the shelves and buildings that we know and love. Perhaps the university library will change from being the largest building on campus to being the largest computing center. The diagram looks very natural to the supercomputing community. It shows several powerful computing systems at three locations, the Internet Archive in San Francisco, Cornell University in upstate New York, and a national supercomputing center under construction in Texas. The locations are linked by very high speed networks; the slowest component is a dedicated 500 mbits/sec link. The first step has been to copy the snapshots of the Web from the Internet Archive to Cornell. The original plan was to copy the files to disk, and deliver them by truck across the country. However, the capacity of the modern research networks Internet2 and the National LambdaRail is so great that the files are being transferred across them electronically. The computer systems fall into several different categories, each tailored for a different function. The Internet Archive's Web collection uses an architecture that is typical of companies providing services over the Web: several thousand small independent servers. The Cornell page store, which is not yet built, will probably use the same architecture to store Web pages. Cornell's structure database is a conventional relational database on a very large database server. When complete, it will store metadata about every Web page in the collection, every URL, and every link between pages. The cluster computers shown at Cornell are shared systems typical of the computers used for large research computations. There is a 128-node Linux cluster and a 128-node Windows cluster. The text indexes at both the Internet Archive and at Cornell use a distributed file system, called Hadoop (http://lucene.apache.org/hadoop/), running on cluster computers. Hadoop is an open source development of ideas from the Google file system. Some tasks, such as building a full text index to the entire collection, are too big for any computer system at either Cornell University or the Internet Archive. The high speed networks allow us to use remote computers at the NSF national supercomputer centers and Amazon (not shown in the figure). Tools and services Any library is more than a collection of artifacts. To make the collections useful, libraries provides tools and services, such as catalogs, bibliographies, classification schemes, abstracting and indexing services, and most importantly knowledgeable reference librarians. These services have evolved over many years, based on a collective view of how the collections will be used. Conversely, the type of research that is carried out in libraries has been shaped by the tools and service that are available. In building a new type of library (or laboratory) there is no experience to guide the development of tools and services. Therefore, the strategy in building the Web Lab is to build a few generic tools, but mainly to work with researchers, to discover what they need, and implement new tools steadily over time. The following list indicates the first generation of tools. Sub-collections User surveys indicate that only few users want to analyze the entire Web. Some people wish to study how selected groups of Web sites have changed across time. Others study a specific domain, e.g., the .edu or .ca domain, or a stratified sample, or pages that have certain features, e.g., contain certain phrases. For these reasons, the most general purpose service provided to researchers is the ability to extract a sub-collection. As an example, many preliminary analyzes are using a sub-collection of 30 million pages extracted from the amazon.com domain in early 2005. Often the sub-collections are small enough that they can be analyzed on the researcher's own computer. Larger sub-collections can be stored on a file server and the cluster computers shown in Figure 2 used for analysis. Focused Web crawling Focused Web crawling is both a useful tool to extract sub-collections and a research topic in its own right. Any Web crawler starts with a set of pages, extracts URLs embedded within them, retrieves the pages that the URLs point to, extracts the URLs from these pages, and continues indefinitely. At each stage, criteria are needed to determine which pages to retrieve next. Focused Web crawling is the special case when the decision making as to where to go next is highly complex. There are practical problems in carrying out crawling experiments on the real Web because it changes continually, and, of course, the real Web cannot be crawled historically. We have modified the Heritrix open-source Web crawler (http://crawler.archive.org/) to crawl the Web Lab database. The Web Graph The Web Graph is the structure formed by the links between pages. Analysis of the Web Graph generalizes concepts of citation analysis to the Web. It has proved to be of great theoretical and practical interest. The best known practical application is PageRank used by Google to rank billions of Web pages by popularity (Brin 1998). Another is Kleinberg's HITS algorithm that ranks pages for their hub and authority values (Kleinberg 1998). A page that links to many pages is a hub, e.g., the Yahoo homepage, and a page that many pages link to is an authority. In addition to studies of the entire Web Graph, we are developing tools to analyze the Web Graph from a sub-collection. Extracting the Web Graph from a collection of pages is a messy task. Many of the URLs embedded in Web pages link to pages that were never collected or never existed. URLs have many variations. Each snapshot of the Web was collected over a period of weeks, if not months, which means that a URL may point at a page that changed before it was collected, or the collection may have two different versions from different dates. Since many researchers face these same challenges, it is worthwhile to invest in general purpose, open source tools. Full-text indexing One of the most important tools that researchers want is a full-text index of the entire collection. Here the problem is scale. The concepts behind Web indexing are well known, even if the practical difficulties are awkward. The basic technique is to create an inverted index of the entire set of pages, which, for every term in the collection, lists the documents in which it occurs. Given a query from a user, the inverted index is used to find all documents that match the terms in the query. Because there may be a huge number of matches, the hits are ranked, with the most highly ranked returned first. The ranking methods combine two types of criteria: similarity between the query and matching documents, and the popularity or importance of documents (using variants of the PageRank algorithm). Commercial companies, such as Google and Yahoo have enormously expensive computer systems to build these indexes. Even so, they index only today's Web, and not all of that. Moreover, they provide a ranking that is tuned for general users and may not be suitable for a specialist research purpose. To build these inverted indexes, the Internet Archive has been experimenting with open source software to index sub-collections. The biggest collection that they have indexed is 750 million records. This is an impressive achievement, but still only one percent of the full collection. At Cornell, we are currently experimenting with this same software to see if it might be possible to use one of the NSF supercomputing centers to index the entire collection. Policies issues on using the Web Lab Although all the information in the Web Lab was originally placed on the Web with open access, there are important policy issues in using such collections for social science research. Advice from the university's lawyers has been very helpful in understanding these issues. We have jointly identified three policy areas. Custodianship of data. The Web collection is an important historical record. The custodial duty is to make no changes to the content of the Web pages and the identifying metadata, such as URL and date. It would be highly improper to destroy inconvenient data, or to tune indexes so that damaging information is never found. Copyright. The Internet Archive and Cornell University are naturally cautions about their responsibility to the owners of copyright material on the Web. The underlying legal concept is that, by placing materials on the Web, owners give an implied license to copy it for limited purposes such as archiving, unless they explicitly state otherwise. This concept has never been tested in the US courts, but would probably withstand a legal challenge. If copyright owners do not want their material in the archive, they can add a robots.txt directory so that it is never collected, or ask the Internet Archive or Cornell to remove it. Privacy. Privacy is another area where the legal framework is still developing. It is clear, however, that mining of Web data can violate privacy by bringing together facts about an individual. Universities have strict policies on experiments that involve human subjects, and the assumption is that these policies apply to data mining of Web data. This places a restraint on all users of the Web Lab. Implications for digital libraries Much of this article is about a single large collection, the Cornell Web Lab, but many of the themes are relevant to all very large digital library collections. Here are some suggested changes that library collections can make to support these new types of research. The first requirement is an application program interface (API) to every collection, so that computer programs can interact with it. Most digital libraries have been developed on the assumption that the user is a person. Much effort is placed in the design of user interfaces, but the computer interface is usually ignored. Today, Google has a public API, but few library collections do. The next requirement is to be able to extract and download sub-collections. In the Web Lab, the scale of the full collection inhibits many analyzes. The usual methodology is to select part of the collection and download it to the researcher's own computer. For this purpose, digital library collections must be designed so people can transfer large parts of them to their own computers. For example, Wikipedia encourages researchers to download everything: the software, the content, the discussions, and all the historical trails. It is approximately one terabyte, a size that can be analyzed on a personal computer. Finally, and perhaps controversially, it should be possible for users to edit metadata, or at least to annotate it. This is contrary to the usual library custom of strict control, but few organizations have the resources to employ people to create item-level metadata for very large collections. (The generosity of the Steven Spielberg in supporting the Survivors of Shoah collection is a rare exception.) Therefore, metadata must be generated automatically, but people should be encouraged to augment it and correct errors. (Crane 2006) describes a successful example. The underlying hypothesis of this paper is that there is a symbiosis between the organization of library collections and the kinds of research that they enable. When electronic texts meet high performance computing, opportunities for new kinds of scholarship emerge, but they need new approaches to how collections are organized and how they are used. Acknowledgements This work would not be possible without the forethought and longstanding commitment of Brewster Kahle and the Internet Archive to capture and preserve the content of the Web for future generations. Past and present members of the Web Lab team include, at Cornell: William Arms, Selcuk Aya, Manuel Calimlim, Pavel Dmitriev, Johannes Gehrke, Dan Huttenlocher, Jon Kleinberg, Blazej Kot, David Lifka, Ruth Mitchell, Lucia Walle, and Felix Weigel; at the Internet Archive: John Aizen, John Berry, Tracey Jacquith, Brewster Kahle, and Michael Stack; with more than 30 masters and undergraduate students. This work is funded in part by National Science Foundation grants CNS-0403340, DUE-0127308, SES-0537606, and IIS 0634677. The Web Lab is an NSF Next Generation Cyberinfrastructure project. Additional support comes from Unisys, Microsoft and Dell. References (Arms 2006a) William Arms, Selcuk Aya, Pavel Dmitriev, Blazej Kot, Ruth Mitchell, and Lucia Walle, A research library based on the historical collections of the Internet Archive. D-Lib Magazine, 12 (2), February 2006. http://www.dlib.org/dlib/february06/arms/02arms.html. (Arms 2006b) William Arms, Selcuk Aya, Pavel Dmitriev, Blazej Kot, Ruth Mitchell, and Lucia Walle, Building a Research Library for the History of the Web. ACM/IEEE Joint Conference on Digital Libraries, 2006. (Backstrom 2006) Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan, Group formation in large social networks: Membership, growth, and evolution. Twelth Intl. Conference on Knowledge Discovery and Data Mining, 2006. (Bergmark 2002) Donna Bergmark, Collection synthesis. ACM/IEEE Joint Conference on Digital Libraries, July, 2002. (Brin 1998) Sergey Brin and Larry Page, The anatomy of a large-scale hypertextual web search engine. Seventh International World Wide Web Conference. Brisbane, Australia, 1998. (Crane 200) Gregory Crane, What do you do with a million books? D-Lib Magazine, 12 (3), March 2006. http://www.dlib.org/dlib/march06/crane/03crane.html. (Kleinberg 1999) Jon Kleinberg, Authoritative sources in a hyperlinked environment. Ninth Ann. ACM-SIAM Symposium on Discrete Algorithm, 1998. (Mitchell 2003) Steve Mitchell, et al., iVia open source virtual library system. D-Lib Magazine, 9(1), January 2003. http://www.dlib.org/dlib/january03/mitchell/01mitchell.html. Keynote address, CaSTA 2006. 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