334 research outputs found
SERGEANT: A Framework for Building More Flexible Web Agents by Exploiting a Search Engine
No full textLearning Database Abstractions For Query Reformulation
No full textions For Query Reformulation Chun-Nan Hsu Craig A. Knoblock Department of Computer Science Information Sciences Institute University of Southern California University of Southern California Los Angeles, CA 90089-0782 4676 Admiralty Way (213) 740-9328 Marina del Rey, CA 90292 [email protected] (310) 822-1511 [email protected] Abstract The query reformulation approach (also called semantic query optimization) takes advantage of the semantic knowledge about the contents of databases for optimization. The basic idea is to use the knowledge to reformulate a query into a less expensive yet equivalent query. Previous work on semantic query optimization has shown the cost reduction that can be achieved by reformulation, we further point out that when applied to distributed multidatabase queries, the reformulation approach can reduce the cost of moving intermediate data from one site to another. However, a robust and efficient method to discover the required knowledge has not yet been develo..
Interoperation in Complex Information Ecosystems (Dagstuhl Seminar 13252)
No full textThis report documents the program and the outcomes of Dagstuhl Seminar 13252 "Interoperation in Complex Information Ecosystems"
Learning Plan Rewriting Rules
No full textPlanning by Rewriting (PbR) is a new paradigm for efficient high-quality planning that exploits plan rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. Despite the advantages of PbR in terms of scalability, plan quality, and anytime behavior, PbR requires the user to define a set of domain-specific plan rewriting rules which can be difficult and time-consuming. This paper presents an approach to automatically learning the plan rewriting rules based on comparing initial and optimal plans. We report results for several planning domains showing that the learned rules are competitive with manually-specified ones, and in several cases the learning algorithm discovered novel rewriting rules. Introduction Planning by Rewriting (PbR) (Ambite & Knoblock 1997; 1998; Ambite 1998) is a planning framework that has shown better scalability than other domainindependent approaches. In a..
Flexible and scalable cost-based query planning in mediators: A transformational approach
No full textAbstractThe Internet provides access to a wealth of information. For any given topic or application domain there are a variety of available information sources. However, current systems, such as search engines or topic directories in the World Wide Web, offer only very limited capabilities for locating, combining, and organizing information. Mediators, systems that provide integrated access and database-like query capabilities to information distributed over heterogeneous sources, are critical to realize the full potential of meaningful access to networked information.Query planning, the task of generating a cost-efficient plan that computes a user query from the relevant information sources, is central to mediator systems. However, query planning is a computationally hard problem due to the large number of possible sources and possible orderings on the operations to process the data. Moreover, the choice of sources, data processing operations, and their ordering, strongly affects the plan cost.In this paper, we present an approach to query planning in mediators based on a general planning paradigm called Planning by Rewriting (PbR) (Ambite and Knoblock, 1997). Our work yields several contributions. First, our PbR-based query planner combines both the selection of the sources and the ordering of the operations into a single search space in which to optimize the plan quality. Second, by using local search techniques our planner explores the combined search space efficiently and produces high-quality plans. Third, because our query planner is an instantiation of a domain-independent framework it is very flexible and can be extended in a principled way. Fourth, our planner has an anytime behavior. Finally, we provide empirical results showing that our PbR-based query planner compares favorably on scalability and plan quality over previous approaches, which include both classical AI planning and dynamic-programming query optimization techniques
A Mixed-Initiative System for Building Mixed-Initiative Systems
No full textMixed-initiative assistants can be applied to a variety of information-rich problem-solving tasks on the Web, such as travel planning and equipment purchasing tasks. A mixed-initiative environment for such tasks can greatly improve the decision making environment for a user if the application is designed to meet the needs of a user. However, each user has different needs and preferences, making it difficult to design a single application for all users. Thus, we are applying the mixed-initiative paradigm recursively to develop a mixed-initiative system for building mixed-initiative systems. This paper describes the basic framework for constructing mixedinitiative systems, which is based on our previous work on developing mixed-initiative information assistants in Heracles. The new system, called Alcmene, will be implemented as an application of Heracles and will allow a user to author a new Heracles application through a mixed-initiative problem-solving process
Exploiting the semantic web for the automatic extraction of Los Angeles city data
No full textLAUREA MAGISTRALEL'Integrazione dei Dati è un passaggio fondamentale nello sviluppo di decisioni basate su di essi nelle aziende e nella maturazione da parte dei manager di una certa consapevolezza della loro importanza. Tuttavia, oggi non è così banale praticare tecniche di Integrazione dei Dati a causa dell'esplosione dal fenomeno dei Big Data, le cui conseguenze stanno sfidando il mondo aziendale in ogni ambito, specialmente il tempo di processo richiesto durante lo sviluppo di qualsiasi progetto. Questo lavoro di ricerca propone un tentativo di soluzione a questo problema e di incoraggiamento ad una più elevata efficacia nelle imprese, suggerendo un nuovo approccio per l'identificazione automatica del contenuto di un dataset, ponendo particolare attenzione sulle informazioni mostrate da una delle sue colonne. Questa categorizzazione intelligente è resa possibile grazie all'utilizzo delle informazioni e dalla struttura che contraddistinguono le ontologie, definite sulla base dei principi del Web Semantico, che consiste in una estensione del World Wide Web. Come punto di partenza del progetto, sono stati testati diversi approcci su 41 dataset appartenenti alla città di Los Angeles. Quindi, durante ogni fase di sviluppo, sono stati tracciati tutti i miglioramenti ottenuti fino a generare una metodologia più completa e ampia che si è identificata in un modello finale. Infine, le performance di quest'ultimo sono state esaminate includendo anche nell'analisi, quando possibile, l'utilizzo di ulteriori informazioni dal dataset, come ad esempio la colonna contenente la posizione. I risultati ottenuti dagli esperimenti sui dataset hanno mostrato un significativo miglioramento dell'accuratezza e correttezza delle risposte, dato dallo sviluppo graduale del modello finale.Data Integration is an essential step for developing data-driven decision in companies and for enhancing the awareness in managers towards the importance of information. However, nowadays is not easy to exert it properly due to the explosion of the Big Data phenomenon, of which consequences are affecting every corner of the enterprise, especially the processing time during projects' development. This research study proposes a solution to address this problem and promote effectiveness in companies, i.e. introduce a novel approach to automatically detect the content of a dataset, with a special attention on the information stored in one of its columns. This intelligent categorization is made possible by the exploitation of ontologies knowledge and structure, according to the principles of the Semantic Web, which is an extension of the World Wide Web. As a starting point, we tested several approaches using 41 datasets belonging to the city of Los Angeles. Hence, we tracked the improvements obtained in each step to design a more comprehensive methodology, i.e. the final model. Finally, we investigated the performance of this final model including also, when available, the exploitation of other information in the datasets, such as the location. Experimental results on datasets has shown that the accuracy and correctness of the outcome improved significantly with the development of the final design
Reformulating Constraint Satisfaction Problems to Improve Scalability
No full textAbstract. Constraint Programming is a powerful approach for modeling and solving many combinatorial problems, scalability, however, remains an issue in practice. Abstraction and reformulation techniques are often sought to overcome the complexity barrier. In this paper we introduce four reformulation techniques that operate on the various components of a Constraint Satisfaction Problem (CSP) in order to reduce the cost of problem solving and facilitate scalability. Our reformulations modify one or more component of the CSP (i.e., the query, variables domains, constraints) and detect symmetrical solutions to avoid generating them. We describe each of these reformulations in the context of CSPs, then evaluate their performance and effects in on the building identification problem introduced by Michalowski and Knoblock [1].
Comment publier les données des musées dans le Linked Open Data ?
No full textCet article décrit le processus de publication des données de l'ensemble de la collection du Smithsonian American Art Museum (SAAM), soit 41000 objets et 8000 artistes, dans le web des données liées et ouvertes. Les chercheurs Pedro Szekely, Craig A. Knoblock, Fengyu Yang, Eleanor E. Fink, Shubham Gupta, Rachel Allen et Georgina Goodlander ont travaillé sur les problématiques suivantes : Comment transformer la base de donnée du SAAM en RDF ? Comment lier le jeu de données du SAAM à d’aut..
Planning, Executing, Sensing, and Replanning for Information Gathering
No full textCurrent specialized planners for query processing are designed to work in local, reliable, and predictable environments. However, a number of problems arise in gathering information from large networks of distributed information. In this environment, the same information may reside in multiple places, actions can be executed in parallel to exploit distributed resources, new goals come into the system during execution, actions may fail due to problems with remote databases or networks, and sensing may need to be interleaved with planning in order to formulate efficient queries. We have developed a planner called Sage that addresses the issues that arise in this environment. This system integrates previous work on planning, execution, replanning, and sensing and extends this work to support simultaneous and interleaved planning and execution. Sage has been applied to the problem of information gathering to provide a flexible and efficient system for integrating heterogeneou..
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