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	Data & Knowledge Engineering Elsevier Science Publishers B. V.         Options: Date Reviewed Title Author Publisher Published Date Descending Order Ascending Order     1-10 of 42 reviews Date Reviewed     Detecting summarizability in OLAP Niemi T., Niinimäki M., Thanisch P., Nummenmaa J. Data & Knowledge Engineering 891-20, 2014.  Type: Article In designing online analytical processing (OLAP) databases or preparing data warehouses providing the bases of OLAP data marts, it is an essential aspect to define index and measure attributes that are suitable for aggregating basic va... Jun 24 2015   An autonomic ontology-based approach to manage information in home-based scenarios: from theory to practice Lasierra N., Alesanco A., O’sullivan D., García J. Data & Knowledge Engineering 87185-205, 2013.  Type: Article Data heterogeneity is a big issue when different health monitoring devices produce data about the same entity. Based on the data, integration is performed and different actions are taken and executed. This paper deals with information ... Mar 18 2014   Towards automatization of domain modeling Reinhartz-Berger I. Data & Knowledge Engineering 69(5): 491-515, 2010.  Type: Article Reinhartz-Berger, in this paper, presents a method for semi-automation of a domain model. Her approach, which is based on the general domain engineering approach, is to create draft models based on families of relevant applications tha... Aug 2 2011   Reasoning with large ontologies stored in relational databases: the OntoMinD approach Al-Jadir L., Parent C., Spaccapietra S. Data & Knowledge Engineering 69(11): 1158-1180, 2010.  Type: Article Efficient ontology implementation is a pending goal in ontology management. The authors of this paper address this problem by presenting OntoMinD, an approach designed to store and manage large ontologies in relational databases.... Mar 15 2011   Approximating sliding windows by cyclic tree-like histograms for efficient range queries Buccafurri F., Lax G. Data & Knowledge Engineering 69(9): 979-997, 2010.  Type: Article Analyzing data streams exhaustively using analytic-type database queries is impractical and simply not viable for most types of applications. For example, sensor-based data mining may generate copious amounts of data. In such situation... Oct 20 2010   Computing intensional answers to questions--an inductive logic programming approach Cimiano P., Rudolph S., Hartfiel H. Data & Knowledge Engineering 69(3): 261-278, 2010.  Type: Article Ordinary database systems answer a query by listing the items that satisfy the query. They can be more helpful, however, if they can point out regularities in the data that was examined while answering the query. In this paper, the aut... Sep 29 2010   Noun retrieval effect on text summarization and delivery of personalized news articles to the user’s desktop Bouras C., Tsogkas V. Data & Knowledge Engineering 69(7): 664-677, 2010.  Type: Article In this paper, text summarization is done by choosing sentences from an article, giving preference to sentences including nouns that either occur frequently or that appear in the article title. The authors also track user behavior to i... Sep 14 2010   Information extraction for search engines using fast heuristic techniques Hong J., Siew E., Egerton S. Data & Knowledge Engineering 69(2): 169-196, 2010.  Type: Article This neatly organized paper presents an approach for designing wrappers that outperform the current state-of-the-art wrappers--ViNT and DEPTA. The main objective of the reported research is to develop an automated nonvisual wr... Aug 12 2010   Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events Wu S., Chen Y. Data & Knowledge Engineering 68(11): 1309-1330, 2009.  Type: Article Sequential pattern mining and temporal pattern mining are very active research areas in the field of data mining, with a wide variety of applications. Data mining searches for patterns that frequently occur in a database of data sequen... Mar 8 2010   Efficiently tracing clusters over high-dimensional on-line data streams Lee J., Park N., Lee W. Data & Knowledge Engineering 68(3): 362-379, 2009.  Type: Article Data evolves over time. Nowadays, learning algorithms should deal efficiently with data streams. Since the mid 90s, more attention has been paid to clustering, with algorithms such as BIRCH [1] and CluStream [2]. Lee, Park, and Lee use... Aug 19 2009       Display 510152550100 per column
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