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

Data Mining - Concepts and Techniques.

Google Earth Engine: Planetary-scale geospatial analysis for everyone

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

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A survey of approaches to automatic schema matching

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

Many web-search queries serve as the beginning of an exploration of an unknown space of information, rather than looking for a specific web page. To answer such queries effectively, the search engine should attempt to organize the space of relevant information in a way that facilitates exploration.

We describe the ASPECTOR system that computes aspects for a given query. Each aspect is a set of search queries that together represent a distinct information need relevant to the original search query. To serve as an effective means to explore the space, ASPECTOR computes aspects that are orthogonal to each other and to have high combined coverage.

ASPECTOR combines two sources of information to compute aspects. We discover candidate aspects by analyzing query logs, and cluster them to eliminate redundancies. We then use a mass-collaboration knowledge base (e.g., Wikipedia) to compute candidate aspects for queries that occur less frequently and to group together aspects that are likely to be "semantically" related. We present a user study that indicates that the aspects we compute are rated favorably against three competing alternatives - related searches proposed by Google, cluster labels assigned by the Clusty search engine, and navigational searches proposed by Bing.

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Identifying aspects for web-search queries

Google Fusion Tables (GFT) brings big data collaboration and visualization to data-experts who possess neither large data-processing resources nor expertise. In this paper we highlight our support for map visualizations over large complex geospatial datasets. Interactive maps created using GFT have already been used by journalists in numerous high-profile stories.

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Big Data Storytelling Through Interactive Maps

Large-scale map visualization systems play an increasingly important role in presenting geographic datasets to end-users Since these datasets can be extremely large, a map rendering system often needs to select a small fraction of the data to visualize them in a limited space This article addresses the fundamental challenge of thinning: determining appropriate samples of data to be shown on specific geographical regions and zoom levels Other than the sheer scale of the data, the thinning problem is challenging because of a number of other reasons: (1) data can consist of complex geographical shapes, (2) rendering of data needs to satisfy certain constraints, such as data being preserved across zoom levels and adjacent regions, and (3) after satisfying the constraints, an optimal solution needs to be chosen based on objectives such as maximality, fairness, and importance of dataThis article formally defines and presents a complete solution to the thinning problem First, we express the problem as an integer programming formulation that efficiently solves thinning for desired objectives Second, we present more efficient solutions for maximality, based on DFS traversal of a spatial tree Third, we consider the common special case of point datasets, and present an even more efficient randomized algorithm Fourth, we show that contiguous regions are tractable for a general version of maximality for which arbitrary regions are intractable Fifth, we examine the structure of our integer programming formulation and show that for point datasets, our program is integral Finally, we have implemented all techniques from this article in Google Maps [Google 2005] visualizations of fusion tables [Gonzalez et al 2010], and we describe a set of experiments that demonstrate the trade-offs among the algorithms

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Consistent thinning of large geographical data for map visualization

One embodiment of the present invention provides a system that facilitates crawling through web-based forms to gather information to facilitate subsequent searches through content which is accessible though the web-based forms. During operation, the system first obtains web-based forms to be searched. Note that the system can obtain these web-based forms from a number of sources. For example, the system can crawl through web sites to identify web-based forms, the system can receive manually provided web-based forms, or the system can find web-based forms through methods other than crawling. Next, the system creates database entries for the identified forms. This involves obtaining and storing metadata describing the identified forms into database entries and then storing these database entries in a form database to facilitate searches through content which is accessible through the identified forms. Note that this form database can include a web index and associated documents, which can be used to facilitate web search queries that return both ordinary documents and documents that result from form queries.

Facilitating searches through content which is accessible through web-based forms

Systems and techniques are provided for receiving an input column and a search keyword and providing one or more suggested columns with which to merge the input column. A coverage score and a refinity score are calculated for potential columns based on the input column as well as a search score based on the search keyword. The one or more suggested columns may be determined based on the coverage score, refinity score, and/or the search score. The input column and/or a potential column may be modified based on a function and the modification may result in a plurality of modified input and/or potential columns. Coverage, refinity, and search scores may be calculated based on the modified columns.

Jayant Madhavan

Papers

Identifying aspects for web-search queries

Big Data Storytelling Through Interactive Maps

Consistent thinning of large geographical data for map visualization

Facilitating searches through content which is accessible through web-based forms

Searching for join candidates