Bio: Ernest Teniente is an academic researcher from Polytechnic University of Catalonia. The author has contributed to research in topics: Data integrity & Conceptual schema. The author has an hindex of 22, co-authored 99 publications receiving 2189 citations.
Papers published on a yearly basis
25 Aug 1997
TL;DR: The technique is proposed reduces the soluticn space by considering only the relevant elements of the multidimensional lattice whose elements represent the solution space of the problem.
Abstract: A multidimensional database is a data repository that supports the efficient execution of complex business decision queries. Query response can be significantly improved by storing an appropriate set of materialized views. These views are selected from the multidimensional lattice whose elements represent the solution space of the problem. Several techniques have been proposed in the past to perform the selection of materialized views for databases with a reduced number of dimensions. When the number and complexity of dimensions increase, the proposed techniques do not scale well. The technique we are proposing reduces the soluticn space by considering only the relevant elements of the multidimensional lattice. An additional statistical analysis allows a further reduction of the solution space.
TL;DR: The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative and employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.
Abstract: Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.
••01 Mar 2012
TL;DR: This paper identifies an expressive fragment of the OCL language that ensures that Description Logics can be appropriately used to reason on UML conceptual schemas with arbitrary OCL constraints and shows how current tools based on different approaches can be used to Reason on conceptual schema enriched with (a decidable fragment of) OCL constraint.
Abstract: To ensure the quality of an information system we must guarantee the correctness of the conceptual schema that represents the knowledge about its domain. The high expressivity of UML schemas annotated with textual OCL constraints enforces the need for automated reasoning techniques. These techniques should be both terminating and complete to be effectively used in practice. In this paper we identify an expressive fragment of the OCL language that ensures these properties. In this way, we overcome the limitations of current techniques when reasoning on such a fragment. As a consequence, we also have that Description Logics can be appropriately used to reason on UML conceptual schemas with arbitrary OCL constraints. We also show how current tools based on different approaches can be used to reason on conceptual schemas enriched with (a decidable fragment of) OCL constraints.
••15 Nov 1999
TL;DR: A general framework is proposed to compare and classify current methods in the field of view updating and integrity constraint maintenance considering how they tackle with both problems and the main drawbacks these methods have.
Abstract: During the process of updating a database, two interrelated problems could arise. On one hand, when an update is applied to the database, integrity constraints could become violated, thus falsifying database consistency. In this case, the integrity constraint maintenance approach tries to obtain additional updates to be applied to re-establish database consistency. On the other hand, when an update request consist on updating some derived predicate, a view updating mechanism must be applied to translate the update request into correct updates on the underlying base facts. In this paper, we propose a general framework to compare and classify current methods in the field of view updating and integrity constraint maintenance. In this sense, we classify them considering how they tackle with both problems and, we also state the main drawbacks these methods have.
TL;DR: These techniques are able to determine, at design-time, when and how each constraint must be checked at runtime to avoid irrelevant verifications and can be integrated in a model-driven development framework to automatically generate a final implementation that automatically checks all constraints in an incremental way.
Abstract: Integrity constraints play a key role in the specification and development of software systems since they state conditions that must always be satisfied by the system at runtime. Therefore, software systems must include some kind of integrity checking component that ensures that all constraints still hold after the execution of any operation that modifies the system state. Integrity checking must be as efficient as possible not to seriously slow down the system performance at runtime. In this sense, this paper proposes a set of techniques to facilitate the efficient integrity checking of UML-based software specifications, usually complemented with a set of integrity constraints defined in Object Constraint Language (OCL) to express all rules that cannot be graphically defined. In particular, our techniques are able to determine, at design-time, when and how each constraint must be checked at runtime to avoid irrelevant verifications. We refer to these techniques as incremental because they minimize the subset of the system state that needs to be checked after each change by assuming that the system was initially in a consistent state and just reevaluating the elements that may have been affected by that change. We also show how the techniques can be integrated in a model-driven development framework to automatically generate a final implementation that automatically checks all constraints in an incremental way.
•08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data
01 Dec 1989
01 Jan 2006
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Abstract: The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.
••01 Dec 2001
TL;DR: The state of the art on the problem of answering queries using views is surveyed, the algorithms proposed to solve it are described, and the disparate works into a coherent framework are synthesized.
Abstract: The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results.
•01 Jan 2004
TL;DR: This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way, and offers the first true synthesis of the field in over a decade.
Abstract: Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs. *Authors are well-recognized experts in the field who have applied the techniques to real-world problems * Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems *Offers the first true synthesis of the field in over a decade Table of Contents 1 Introduction * 2 The Language of First-Order Logic *3 Expressing Knowledge * 4 Resolution * 5 Horn Logic * 6 Procedural Control of Reasoning * 7 Rules in Production Systems * 8 Object-Oriented Representation * 9 Structured Descriptions * 10 Inheritance * 11 Numerical Uncertainty *12 Defaults *13 Abductive Reasoning *14 Actions * 15 Planning *16 A Knowledge Representation Tradeoff * Bibliography * Index