Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994734
Haixun Wang, C. Zaniolo
A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new applications domains, such as data mining. In this paper, we solve this problem by stream-oriented aggregate functions and generalized table functions which are definable by users in the SQL language itself, rather than in an external programming language. These simple extensions turn SQL into a powerful database language, which can express a wide range of applications, including recursive queries, ROLAP (relational online analytical processing) aggregates, time-series queries, stream-oriented processing and data-mining functions. The SQL extensions are implemented in ATLaS (Aggregate and Table Language and System).
{"title":"The ATLaS system and its powerful database language based on simple extensions of SQL","authors":"Haixun Wang, C. Zaniolo","doi":"10.1109/ICDE.2002.994734","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994734","url":null,"abstract":"A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new applications domains, such as data mining. In this paper, we solve this problem by stream-oriented aggregate functions and generalized table functions which are definable by users in the SQL language itself, rather than in an external programming language. These simple extensions turn SQL into a powerful database language, which can express a wide range of applications, including recursive queries, ROLAP (relational online analytical processing) aggregates, time-series queries, stream-oriented processing and data-mining functions. The SQL extensions are implemented in ATLaS (Aggregate and Table Language and System).","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130537475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994770
Mengzhi Wang, N. Chan, S. Papadimitriou, C. Faloutsos, T. Madhyastha
Network, Web, and disk I/O traffic are usually bursty and self-similar and therefore cannot be modeled adequately with Poisson arrivals. However, we wish to model these types of traffic and generate realistic traces, because of obvious applications for disk scheduling, network management, and Web server design. Previous models (like fractional Brownian motion and FARIMA, etc.) tried to capture the 'burstiness'. However, the proposed models either require too many parameters to fit and/or require prohibitively large (quadratic) time to generate large traces. We propose a simple, parsimonious method, the b-model, which solves both problems: it requires just one parameter, and can easily generate large traces. In addition, it has many more attractive properties: (a) with our proposed estimation algorithm, it requires just a single pass over the actual trace to estimate b. For example, a one-day-long disk trace in milliseconds contains about 86 Mb data points and requires about 3 minutes for model fitting and 5 minutes for generation. (b) The resulting synthetic traces are very realistic: our experiments on real disk and Web traces show that our synthetic traces match the real ones very well in terms of queuing behavior.
{"title":"Data mining meets performance evaluation: fast algorithms for modeling bursty traffic","authors":"Mengzhi Wang, N. Chan, S. Papadimitriou, C. Faloutsos, T. Madhyastha","doi":"10.1109/ICDE.2002.994770","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994770","url":null,"abstract":"Network, Web, and disk I/O traffic are usually bursty and self-similar and therefore cannot be modeled adequately with Poisson arrivals. However, we wish to model these types of traffic and generate realistic traces, because of obvious applications for disk scheduling, network management, and Web server design. Previous models (like fractional Brownian motion and FARIMA, etc.) tried to capture the 'burstiness'. However, the proposed models either require too many parameters to fit and/or require prohibitively large (quadratic) time to generate large traces. We propose a simple, parsimonious method, the b-model, which solves both problems: it requires just one parameter, and can easily generate large traces. In addition, it has many more attractive properties: (a) with our proposed estimation algorithm, it requires just a single pass over the actual trace to estimate b. For example, a one-day-long disk trace in milliseconds contains about 86 Mb data points and requires about 3 minutes for model fitting and 5 minutes for generation. (b) The resulting synthetic traces are very realistic: our experiments on real disk and Web traces show that our synthetic traces match the real ones very well in terms of queuing behavior.","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"55 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120923594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994718
S. Flesca, F. Furfaro, S. Greco
Informally presents the query language /spl Xscr//spl Gscr//spl Lscr/ (eXtensible Graphical Language). The main features of the language are described by means of two queries on a document named "bib.xml" (a document describing the bibliographic details of a book).
{"title":"A graphical XML query language","authors":"S. Flesca, F. Furfaro, S. Greco","doi":"10.1109/ICDE.2002.994718","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994718","url":null,"abstract":"Informally presents the query language /spl Xscr//spl Gscr//spl Lscr/ (eXtensible Graphical Language). The main features of the language are described by means of two queries on a document named \"bib.xml\" (a document describing the bibliographic details of a book).","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121577240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994777
C. Aggarwal
Nearest neighbor search is an important and widely used problem in a number of important application domains. In many of these domains, the dimensionality of the data representation is often very high. Recent theoretical results have shown that the concept of proximity or nearest neighbors may not be very meaningful for the high dimensional case. Therefore, it is often a complex problem to find good quality nearest neighbors in such data sets. Furthermore, it is also difficult to judge the value and relevance of the returned results. In fact, it is hard for any fully automated system to satisfy a user about the quality of the nearest neighbors found unless he is directly involved in the process. This is especially the case for high dimensional data in which the meaningfulness of the nearest neighbors found is questionable. We address the complex problem of high dimensional nearest neighbor search from the user perspective by designing a system which uses effective cooperation between the human and the computer. The system provides the user with visual representations of carefully chosen subspaces of the data in order to repeatedly elicit his preferences about the data patterns which are most closely related to the query point. These preferences are used in order to determine and quantify the meaningfulness of the nearest neighbors. Our system is not only able to find and quantify the meaningfulness of the nearest neighbors, but is also able to diagnose situations in which the nearest neighbors found are truly not meaningful.
{"title":"Towards meaningful high-dimensional nearest neighbor search by human-computer interaction","authors":"C. Aggarwal","doi":"10.1109/ICDE.2002.994777","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994777","url":null,"abstract":"Nearest neighbor search is an important and widely used problem in a number of important application domains. In many of these domains, the dimensionality of the data representation is often very high. Recent theoretical results have shown that the concept of proximity or nearest neighbors may not be very meaningful for the high dimensional case. Therefore, it is often a complex problem to find good quality nearest neighbors in such data sets. Furthermore, it is also difficult to judge the value and relevance of the returned results. In fact, it is hard for any fully automated system to satisfy a user about the quality of the nearest neighbors found unless he is directly involved in the process. This is especially the case for high dimensional data in which the meaningfulness of the nearest neighbors found is questionable. We address the complex problem of high dimensional nearest neighbor search from the user perspective by designing a system which uses effective cooperation between the human and the computer. The system provides the user with visual representations of carefully chosen subspaces of the data in order to repeatedly elicit his preferences about the data patterns which are most closely related to the query point. These preferences are used in order to determine and quantify the meaningfulness of the nearest neighbors. Our system is not only able to find and quantify the meaningfulness of the nearest neighbors, but is also able to diagnose situations in which the nearest neighbors found are truly not meaningful.","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123090367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994711
I. Popivanov, Renée J. Miller
Considers the use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity searching over high-dimensional time-series data. While numerous transformations have been proposed and studied, the only wavelet that has been shown to be effective for this application is the Haar wavelet. In this work, we observe that a large class of wavelet transformations (not only orthonormal wavelets but also bi-orthonormal wavelets) can be used to support similarity searching. This class includes the most popular and most effective wavelets being used in image compression. We present a detailed performance study of the effects of using different wavelets on the performance of similarity searching for time-series data. We include several wavelets that outperform both the Haar wavelet and the best-known non-wavelet transformations for this application. To ensure our results are usable by an application engineer, we also show how to configure an indexing strategy for the best-performing transformations. Finally, we identify classes of data that can be indexed efficiently using these wavelet transformations.
{"title":"Similarity search over time-series data using wavelets","authors":"I. Popivanov, Renée J. Miller","doi":"10.1109/ICDE.2002.994711","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994711","url":null,"abstract":"Considers the use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity searching over high-dimensional time-series data. While numerous transformations have been proposed and studied, the only wavelet that has been shown to be effective for this application is the Haar wavelet. In this work, we observe that a large class of wavelet transformations (not only orthonormal wavelets but also bi-orthonormal wavelets) can be used to support similarity searching. This class includes the most popular and most effective wavelets being used in image compression. We present a detailed performance study of the effects of using different wavelets on the performance of similarity searching for time-series data. We include several wavelets that outperform both the Haar wavelet and the best-known non-wavelet transformations for this application. To ensure our results are usable by an application engineer, we also show how to configure an indexing strategy for the best-performing transformations. Finally, we identify classes of data that can be indexed efficiently using these wavelet transformations.","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128444123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994763
Yuhui Jin, Sichun Xu, S. Decker, G. Wiederhold
OntoWebber is a system for managing data on the Web with formally encoded semantics. It aims at solving the problems current technologies are confronted with, namely, the reusability of software components, flexibility in personalization, and ease of maintenance for data intensive Web sites. Based on a domain ontology and a site modeling ontology, site views on the underlying data can be constructed as site models. Instantiation of these models will create a browsable Web site, and manipulation of the site models helps to reduce the high effort of personalizing and maintaining the Web site. In this paper we present the architecture and demonstrate the major components of the system.
{"title":"OntoWebber: a novel approach for managing data on the Web","authors":"Yuhui Jin, Sichun Xu, S. Decker, G. Wiederhold","doi":"10.1109/ICDE.2002.994763","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994763","url":null,"abstract":"OntoWebber is a system for managing data on the Web with formally encoded semantics. It aims at solving the problems current technologies are confronted with, namely, the reusability of software components, flexibility in personalization, and ease of maintenance for data intensive Web sites. Based on a domain ontology and a site modeling ontology, site views on the underlying data can be constructed as site models. Instantiation of these models will create a browsable Web site, and manipulation of the site models helps to reduce the high effort of personalizing and maintaining the Web site. In this paper we present the architecture and demonstrate the major components of the system.","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123034768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994733
Peter A. Tucker, D. Maier
Applications that process data streams are becoming common. These applications are often queries over streams, so it seems natural to use a database management system instead of a custom application. However, some traditional relational operators are not conducive to stream processing. We propose embedding punctuations into data streams. A punctuation is a predicate that describes a subset of tuples. It informs a stream processor that no tuples exist after that punctuation that satisfy its predicate.
{"title":"Exploiting punctuation semantics in data streams","authors":"Peter A. Tucker, D. Maier","doi":"10.1109/ICDE.2002.994733","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994733","url":null,"abstract":"Applications that process data streams are becoming common. These applications are often queries over streams, so it seems natural to use a database management system instead of a custom application. However, some traditional relational operators are not conducive to stream processing. We propose embedding punctuations into data streams. A punctuation is a predicate that describes a subset of tuples. It informs a stream processor that no tuples exist after that punctuation that satisfy its predicate.","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"1606 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121549487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994746
Sergej Sizov, Stefan Siersdorfer, M. Theobald, G. Weikum
The BINGO! system implements an approach to focused crawling that aims to overcome the limitations of the initial training data. To this end, BINGO! identifies, among the crawled and positively classified documents of a topic, characteristic "archetypes" and uses them for periodically re-training the classifier; this way the crawler is dynamically adapted based on the most significant documents seen so far. Two kinds of archetypes are considered: good authorities as determined by employing Kleinberg's link analysis algorithm, and documents that have been automatically classified with high confidence using a linear SVM classifier.
{"title":"The BINGO! focused crawler: from bookmarks to archetypes","authors":"Sergej Sizov, Stefan Siersdorfer, M. Theobald, G. Weikum","doi":"10.1109/ICDE.2002.994746","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994746","url":null,"abstract":"The BINGO! system implements an approach to focused crawling that aims to overcome the limitations of the initial training data. To this end, BINGO! identifies, among the crawled and positively classified documents of a topic, characteristic \"archetypes\" and uses them for periodically re-training the classifier; this way the crawler is dynamically adapted based on the most significant documents seen so far. Two kinds of archetypes are considered: good authorities as determined by employing Kleinberg's link analysis algorithm, and documents that have been automatically classified with high confidence using a linear SVM classifier.","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130678876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994767
Hae Don Chon, D. Agrawal, A. E. Abbadi
With the advances in wireless communications and mobile device technologies, location-based applications or services will become an essential part of future applications. We have developed a location-based application called NAPA (Nearest Available Parking lot Application) that assists users to find the nearest parking space on campus. NAPA is an example of an application which combines a number of new features, such as location-based, wireless communication and a directory service like LDAP (Lightweight Directory Access Protocol).
{"title":"NAPA : Nearest Available Parking lot Application","authors":"Hae Don Chon, D. Agrawal, A. E. Abbadi","doi":"10.1109/ICDE.2002.994767","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994767","url":null,"abstract":"With the advances in wireless communications and mobile device technologies, location-based applications or services will become an essential part of future applications. We have developed a location-based application called NAPA (Nearest Available Parking lot Application) that assists users to find the nearest parking space on campus. NAPA is an example of an application which combines a number of new features, such as location-based, wireless communication and a directory service like LDAP (Lightweight Directory Access Protocol).","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131827073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/ICDE.2002.994742
R. Lawrence, K. Barker
Unity is an architecture for integrating relational databases which performs three processes: meta-data capture, semantic integration, and query formulation and execution. The foundation of the architecture is a naming methodology that allows concepts to be integrated across systems. Semantic naming of schema constructs increases automation during integration and provides users with physical and logical access transparency during query formulation.
{"title":"Using Unity to semi-automatically integrate relational schema","authors":"R. Lawrence, K. Barker","doi":"10.1109/ICDE.2002.994742","DOIUrl":"https://doi.org/10.1109/ICDE.2002.994742","url":null,"abstract":"Unity is an architecture for integrating relational databases which performs three processes: meta-data capture, semantic integration, and query formulation and execution. The foundation of the architecture is a naming methodology that allows concepts to be integrated across systems. Semantic naming of schema constructs increases automation during integration and provides users with physical and logical access transparency during query formulation.","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129537717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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