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Conference

Extending Database Technology 

About: Extending Database Technology is an academic conference. The conference publishes majorly in the area(s): Query optimization & Query language. Over the lifetime, 1795 publications have been published by the conference receiving 54996 citations.


Papers
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Book ChapterDOI
25 Mar 1996
TL;DR: This work adds time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern, and relax the restriction that the items in an element of a sequential pattern must come from the same transaction.
Abstract: The problem of mining sequential patterns was recently introduced in [3] We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items The problem is to discover all sequential patterns with a user-specified minimum support, where the support of a pattern is the number of data-sequences that contain the pattern An example of a sequential pattern is“5% of customers bought ‘Foundation’ and ‘Ringworld’ in one transaction, followed by ‘Second Foundation’ in a later transaction” We generalize the problem as follows First, we add time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern Second, we relax the restriction that the items in an element of a sequential pattern must come from the same transaction, instead allowing the items to be present in a set of transactions whose transaction-times are within a user-specified time window Third, given a user-defined taxonomy (is-a hierarchy) on items, we allow sequential patterns to include items across all levels of the taxonomy

2,973 citations

Book ChapterDOI
25 Mar 1996
TL;DR: Issues in building a scalable classifier are discussed and the design of SLIQ, a new classifier that uses a novel pre-sorting technique in the tree-growth phase to enable classification of disk-resident datasets is presented.
Abstract: Classification is an important problem in the emerging field of data mining Although classification has been studied extensively in the past, most of the classification algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets This paper discusses issues in building a scalable classifier and presents the design of SLIQ, a new classifier SLIQ is a decision tree classifier that can handle both numeric and categorical attributes It uses a novel pre-sorting technique in the tree-growth phase This sorting procedure is integrated with a breadth-first tree growing strategy to enable classification of disk-resident datasets SLIQ also uses a new tree-pruning algorithm that is inexpensive, and results in compact and accurate trees The combination of these techniques enables SLIQ to scale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining

860 citations

Book ChapterDOI
23 Mar 1998
TL;DR: This work presents an approach for a system that constructs process models from logs of past, unstructured executions of the given process, and presents results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation.
Abstract: Modern enterprises increasingly use the workflow paradigm to prescribe how business processes should be performed. Processes are typically modeled as annotated activity graphs. We present an approach for a system that constructs process models from logs of past, unstructured executions of the given process. The graph so produced conforms to the dependencies and past executions present in the log. By providing models that capture the previous executions of the process, this technique allows easier introduction of a workflow system and evaluation and evolution of existing process models. We also present results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation.

742 citations

Book ChapterDOI
14 Mar 2004
TL;DR: A method is proposed that, given a query submitted to a search engine, suggests a list of related queries that are based in previously issued queries and can be issued by the user to the search engine to tune or redirect the search process.
Abstract: In this paper we propose a method that, given a query submitted to a search engine, suggests a list of related queries The related queries are based in previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process The method proposed is based on a query clustering process in which groups of semantically similar queries are identified The clustering process uses the content of historical preferences of users registered in the query log of the search engine The method not only discovers the related queries, but also ranks them according to a relevance criterion Finally, we show with experiments over the query log of a search engine the effectiveness of the method.

656 citations

Proceedings ArticleDOI
21 Mar 2011
TL;DR: This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications, and crystallizes the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.
Abstract: Scalable database management systems (DBMS)---both for update intensive application workloads as well as decision support systems for descriptive and deep analytics---are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its own set of novel challenges that must be addressed to ensure the success of data management solutions in the cloud environment. This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. Our background study encompasses both classes of systems: (i) for supporting update heavy applications, and (ii) for ad-hoc analytics and decision support. We then focus on providing an in-depth analysis of systems for supporting update intensive web-applications and provide a survey of the state-of-the-art in this domain. We crystallize the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.

566 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202154
202082
201996
201885
201788
2016106