Data-Intensive Text Processing with MapReduce. Jimmy LinЧитать онлайн книгу.
of user data, and the other with 6.5 petabytes of user data spanning 170 trillion records and growing by 150 billion new records per day. Shortly thereafter, Facebook revealed2 similarly impressive numbers, boasting of 2.5 petabytes of user data, growing at about 15 terabytes per day. Petabyte datasets are rapidly becoming the norm, and the trends are clear: our ability to store data is fast overwhelming our ability to process what we store. More distressing, increases in capacity are outpacing improvements in bandwidth such that our ability to even read back what we store is deteriorating [91]. Disk capacities have grown from tens of megabytes in the mid-1980s to about a couple of terabytes today (several orders of magnitude). On the other hand, latency and bandwidth have improved relatively little: in the case of latency, perhaps 2× improvement during the last quarter century, and in the case of bandwidth, perhaps 50×. Given the tendency for individuals and organizations to continuously fill up whatever capacity is available, large-data problems are growing increasingly severe.
Moving beyond the commercial sphere, many have recognized the importance of data management in many scientific disciplines, where petabyte-scale datasets are also becoming increasingly common [21]. For example:
• The high-energy physics community was already describing experiences with petabyte-scale databases back in 2005 [20]. Today, the Large Hadron Collider (LHC) near Geneva is the world’s largest particle accelerator, designed to probe the mysteries of the universe, including the fundamental nature of matter, by recreating conditions shortly following the Big Bang. When it becomes fully operational, the LHC will produce roughly 15 petabytes of data a year.3
• Astronomers have long recognized the importance of a “digital observatory” that would support the data needs of researchers across the globe—the Sloan Digital Sky Survey [145] is perhaps the most well known of these projects. Looking into the future, the Large Synoptic Survey Telescope (LSST) is a wide-field instrument that is capable of observing the entire sky every few days. When the telescope comes online around 2015 in Chile, its 3.2 gigapixel primary camera will produce approximately half a petabyte of archive images every month [19].
• The advent of next-generation DNA sequencing technology has created a deluge of sequence data that needs to be stored, organized, and delivered to scientists for further study. Given the fundamental tenant in modern genetics that genotypes explain phenotypes, the impact of this technology is nothing less than transformative [103]. The European Bioinformatics Institute (EBI), which hosts a central repository of sequence data called EMBL-bank, has increased storage capacity from 2.5 petabytes in 2008 to 5 petabytes in 2009 [142]. Scientists are predicting that, in the not-so-distant future, sequencing an individual’s genome will be no more complex than getting a blood test today—ushering a new era of personalized medicine, where interventions can be specifically targeted for an individual.
Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate, explore, and mine massive datasets [72]—this has been hailed as the emerging “fourth paradigm” of science [73] (complementing theory, experiments, and simulations). In other areas of academia, particularly computer science, systems and algorithms incapable of scaling to massive real-world datasets run the danger of being dismissed as “toy systems” with limited utility. Large data is a fact of today’s world and data-intensive processing is fast becoming a necessity, not merely a luxury or curiosity.
Although large data comes in a variety of forms, this book is primarily concerned with processing large amounts of text, but touches on other types of data as well (e.g., relational and graph data). The problems and solutions we discuss mostly fall into the disciplinary boundaries of natural language processing (NLP) and information retrieval (IR). Recent work in these fields is dominated by a data-driven, empirical approach, typically involving algorithms that attempt to capture statistical regularities in data for the purposes of some task or application. There are three components to this approach: data, representations of the data, and some method for capturing regularities in the data. Data are called corpora (singular, corpus) by NLP researchers and collections by those from the IR community. Aspects of the representations of the data are called features, which may be “superficial” and easy to extract, such as the words and sequences of words themselves, or “deep” and more difficult to extract, such as the grammatical relationship between words. Finally, algorithms or models are applied to capture regularities in the data in terms of the extracted features for some application. One common application, classification, is to sort text into categories. Examples include: Is this email spam or not spam? Is this word part of an address or a location? The first task is easy to understand, while the second task is an instance of what NLP researchers call named-entity detection [138], which is useful for local search and pinpointing locations on maps. Another common application is to rank texts according to some criteria—search is a good example, which involves ranking documents by relevance to the user’s query. Another example is to automatically situate texts along a scale of “happiness”, a task known as sentiment analysis or opinion mining [118], which has been applied to everything from understanding political discourse in the blogosphere to predicting the movement of stock prices.
There is a growing body of evidence, at least in text processing, that of the three components discussed above (data, features, algorithms), data probably matters the most. Superficial word-level features coupled with simple models in most cases trump sophisticated models with deeper features and less data. But why can’t we have our cake and eat it too? Why not both sophisticated models and deep features applied to lots of data? Because inference over sophisticated models and extraction of deep features are often computationally intensive, they don’t scale well.
Consider a simple task such as determining the correct usage of easily confusable words such as “than” and “then” in English. One can view this as a supervised machine learning problem: we can train a classifier to disambiguate between the options, and then apply the classifier to new instances of the problem (say, as part of a grammar checker). Training data is fairly easy to come by—we can just gather a large corpus of texts and assume that most writers make correct choices (the training data may be noisy, since people make mistakes, but no matter). In 2001, Banko and Brill [14] published what has become a classic paper in natural language processing exploring the effects of training data size on classification accuracy, using this task as the specific example. They explored several classification algorithms (the exact ones aren’t important, as we shall see), and not surprisingly, found that more data led to better accuracy. Across many different algorithms, the increase in accuracy was approximately linear in the log of the size of the training data. Furthermore, with increasing amounts of training data, the accuracy of different algorithms converged, such that pronounced differences in effectiveness observed on smaller datasets basically disappeared at scale. This led to a somewhat controversial conclusion (at least at the time): machine learning algorithms really don’t matter, all that matters is the amount of data you have. This resulted in an even more controversial recommendation, delivered somewhat tongue-in-cheek: we should just give up working on algorithms and simply spend our time gathering data (while waiting for computers to become faster so we can process the data).
As another example, consider the problem of answering short, fact-based questions such as “Who shot Abraham Lincoln?” Instead of returning a list of documents that the user would then have to sort through, a question answering (QA) system would directly return the answer: John Wilkes Booth. This problem gained interest in the late 1990s, when natural language processing researchers approached the challenge with sophisticated linguistic processing techniques such as syntactic and semantic analysis. Around 2001, researchers discovered a far simpler approach to answering such questions based on pattern matching [27; 53; 92]. Suppose you wanted the answer to the above question. As it turns out, you can simply search for the phrase “shot Abraham Lincoln” on the web and look for what appears to its left. Or better yet, look through multiple instances of this phrase and tally up the words that appear to the left. This simple strategy works surprisingly well, and has become known as the redundancy-based approach to question answering. It capitalizes on the insight that in a very large text collection (i.e., the web), answers to commonly asked questions will be stated in obvious ways, such that pattern-matching techniques suffice to extract answers accurately.
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