In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

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The anatomy of a large-scale hypertextual Web search engine

Imagined communities: Reflections on the origin and spread of nationalism

The Anatomy of a Large-Scale Hypertextual Web Search Engine.

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

A promising approach to scaling Web applications is to distribute the server infrastructure on which they run. This approach, unfortunately, can introduce latency between the application and database servers, which in turn increases the network latency of Web interactions for the clients (end users). In this paper we introduce the concept of source-to-source holistic transformations---transformations that seek to optimize both the application code and the database requests made by it, to reduce clientlatency. As examples of our concept, we propose and evaluate two source-to-source holistic transformations that focus on hiding the latencies of database queries. We argue that opportunities for applying these transformations will continue to exist in Web applications. We then present algorithms for automating these transformations in asource-to-source compiler. Finally, we evaluate the effect of these two transformations on three realistic Web benchmark applications, both in the traditional centralized setting and a distributed setting.

/pdf/holistic-query-transformations-for-dynamic-web-applications-1e5vie5akq.pdf

Holistic Query Transformations for Dynamic Web Applications

Participatory sensing systems (PSS) require frequent injection of information that has a short shelf-life. The use of crowds to gather information for PSS is therefore particularly challenging. In this study, we explore the impact of two policies on user contributions. A quid-pro-quo policy exchanges contributions from users for access to critical information in the system. A request policy simply reminds the user that information is needed to make the system function well. Prior research has shown that request for help in crowdsourced system is an effective mechanism to increase contributions. During a large-scale experimental study within a publicly deployed, crowdsourced, transit information system, we analyzed metrics associated with frequency of contribution and commitment to long-term use over a 10-month period. Our results confirmed that quid-pro-quo led to more contribution, but at a cost of faster departure from the study. When a participant was simply requested to contribute, but could still access community-generated data if they ignored a request, was largely ineffective and was statistically similar to the control condition where no request for contribution occurred. Thus crowdsource system designers should consider imposing quid-pro-quo type policies for PSS that concentrate on fewer users, but makes them more productive.

/pdf/motivating-contribution-in-a-participatory-sensing-system-3grtkvplvg.pdf

Motivating contribution in a participatory sensing system via quid-pro-quo

Much of the world's information is stored electronically in data sources The data sources can be full-fledged databases, simple files, HTML pages or specialized data sources that possess diverse query processing capabilities The common architecture to integrate such sources consists of {\it mediators} that give a global view over the content of all sources, and {\it wrappers} that give a local view of each source Answering queries in this architecture is a difficult problem due to the wide range of capabilities of data sources This paper presents a solution to this problem in the context of the \Duery processor We provide a tool to the wrapper implementor to describe the capabilities of the wrapper in fine detail When a wrapper is registered with the mediator, the mediator uploads the capabilities of the wrapper, and smoothly integrates these capabilities into query processing Our solution is novel both in the level of detail permitted by the tool and its easy incorporation into existing query optimization strategies In this paper we describe: the query processing of \Dthe language for specifying wrapper capabilities, the algorithms that integrates these capabilities into query processing, and an implementation of these techniques in the \Drototype

https://hal.inria.fr/inria-00073551/document

Dealing with Discrepancies in Wrapper Functionality

Current main memory database system architectures are still challenged by high contention workloads and this challenge will continue to grow as the number of cores in processors continues to increase [23]. These systems schedule transactions randomly across cores to maximize concurrency and to produce a uniform load across cores. Scheduling never considers potential conflicts. Performance could be improved if scheduling balanced between concurrency to maximize throughput and scheduling transactions linearly to avoid conflicts. In this paper, we present the design of several intelligent transaction scheduling algorithms that consider both potential transaction conflicts and concurrency. To incorporate reasoning about transaction conflicts, we develop a supervised machine learning model that estimates the probability of conflict. This model is incorporated into several scheduling algorithms. In addition, we integrate an unsupervised machine learning algorithm into an intelligent scheduling algorithm. We then empirically measure the performance impact of different scheduling algorithms on OLTP and social networking workloads. Our results show that, with appropriate settings, intelligent scheduling can increase throughput by 54% and reduce abort rate by 80% on a 20-core machine, relative to random scheduling. In summary, the paper provides preliminary evidence that intelligent scheduling significantly improves DBMS performance.

Scheduling OLTP transactions via learned abort prediction

A very large number of data sources on environment, energy, and natural resources are available worldwide. Unfortunately, users usually face several problems when they want to search and use environmental information. In this paper, we analyze these problems. We describe a conceptual analysis of the four major tasks in the production of environmental data, from the technology point of view, and describe the organization of the data that results from these tasks. We then discuss the notion of metainformation and outline an architecture for environmental data systems that formally models metadata and addresses some of the major problems faced by users.

/pdf/improving-access-to-environmental-data-using-context-8u3v373gsc.pdf

Anthony Tomasic

Papers

Holistic Query Transformations for Dynamic Web Applications

Motivating contribution in a participatory sensing system via quid-pro-quo

Dealing with Discrepancies in Wrapper Functionality

Scheduling OLTP transactions via learned abort prediction

Improving access to environmental data using context information