The resource lookup requirements in applications such as web caching, web content search, content distribution, resource sharing, network monitoring and management, and e-commerce have caught the attention of peer-to-peer (P2P) distributed systems researchers. Over the past few years, several decentralized P2P lookup system designs have been proposed for addressing these requirements. Most of these early designs are targeted at specific applications. Unfortunately, the variations in the operating environments and lookup characteristics across applications restricts the applicability of such specialized designs. In this paper, we present an architecture for P2P systems that identifies the functions necessary for designing resource lookup systems with wide applicability. We demonstrate the usefulness of the functions included in the architecture by illustrating their use in developing diverse lookup techniques.
{"title":"A Peer-to-Peer Architecture to Enable Versatile Lookup System Design","authors":"Vivek Sawant, J. Kaur","doi":"10.1109/ICDEW.2006.17","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.17","url":null,"abstract":"The resource lookup requirements in applications such as web caching, web content search, content distribution, resource sharing, network monitoring and management, and e-commerce have caught the attention of peer-to-peer (P2P) distributed systems researchers. Over the past few years, several decentralized P2P lookup system designs have been proposed for addressing these requirements. Most of these early designs are targeted at specific applications. Unfortunately, the variations in the operating environments and lookup characteristics across applications restricts the applicability of such specialized designs. In this paper, we present an architecture for P2P systems that identifies the functions necessary for designing resource lookup systems with wide applicability. We demonstrate the usefulness of the functions included in the architecture by illustrating their use in developing diverse lookup techniques.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115208294","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}
Ken'ichi Ishikawa, Atsuyuki Morishima, S. Sugimoto
Today, more and more people in knowledge communities, like research laboratories, use shared file servers to store and share their information. People in such communities often work together and their files stored in a file server have relationships with each other. Information on the relationships is usually exchanged offline and used implicitly to facilitate the management and sharing of the files. This paper proposes new functions to manage and use the relationships to make various views on the file servers. The functions provide a high-level support and are compatible with the operational framework of existing file systems.
{"title":"New Functions of File Systems to Manage Information Shared by Communities","authors":"Ken'ichi Ishikawa, Atsuyuki Morishima, S. Sugimoto","doi":"10.1109/ICDEW.2006.98","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.98","url":null,"abstract":"Today, more and more people in knowledge communities, like research laboratories, use shared file servers to store and share their information. People in such communities often work together and their files stored in a file server have relationships with each other. Information on the relationships is usually exchanged offline and used implicitly to facilitate the management and sharing of the files. This paper proposes new functions to manage and use the relationships to make various views on the file servers. The functions provide a high-level support and are compatible with the operational framework of existing file systems.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115897431","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}
Steven P. Callahan, J. Freire, E. Santos, C. Scheidegger, Cláudio T. Silva, H. Vo
Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypotheses, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Data exploration through visualization requires scientists to go through several steps. In essence, they need to assemble complex workflows that consist of dataset selection, specification of series of operations that need to be applied to the data, and the creation of appropriate visual representations, before they can finally view and analyze the results. Often, insight comes from comparing the results of multiple visualizations that are created during the data exploration process.
{"title":"Managing the Evolution of Dataflows with VisTrails","authors":"Steven P. Callahan, J. Freire, E. Santos, C. Scheidegger, Cláudio T. Silva, H. Vo","doi":"10.1109/ICDEW.2006.75","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.75","url":null,"abstract":"Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypotheses, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Data exploration through visualization requires scientists to go through several steps. In essence, they need to assemble complex workflows that consist of dataset selection, specification of series of operations that need to be applied to the data, and the creation of appropriate visual representations, before they can finally view and analyze the results. Often, insight comes from comparing the results of multiple visualizations that are created during the data exploration process.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116254380","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}
Just as the link structure of the web is a critical component in today's web search, complex relationships (i.e., the different ways the dots are connected) will be an important component in tomorrow's web search technologies. In this paper, I summarize my research on answering the question of: How we can exploit semantic relationships of named-entities to improve relevance in search and ranking of documents? The intuition of my approach is to first analyze the relationships of namedentities with respect to a query. Second, relevance weights, which are assigned by human experts, can then be used to guarantee results within a relevance threshold. These relevance measures can be applied both for searching and ranking of documents.
{"title":"Searching and Ranking Documents based on Semantic Relationships","authors":"Boanerges Aleman-Meza","doi":"10.1109/ICDEW.2006.131","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.131","url":null,"abstract":"Just as the link structure of the web is a critical component in today's web search, complex relationships (i.e., the different ways the dots are connected) will be an important component in tomorrow's web search technologies. In this paper, I summarize my research on answering the question of: How we can exploit semantic relationships of named-entities to improve relevance in search and ranking of documents? The intuition of my approach is to first analyze the relationships of namedentities with respect to a query. Second, relevance weights, which are assigned by human experts, can then be used to guarantee results within a relevance threshold. These relevance measures can be applied both for searching and ranking of documents.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116293418","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}
Applying conventional sequential pattern mining methods to text data extracts many uninteresting patterns, which increases the time to interpret the extracted patterns. To solve this problem, we propose a new sequential pattern mining algorithm by adopting the following two constraints. One is to select sequences with regard to item intervals--the number of items between any two adjacent items in a sequence--and the other is to select sequences with regard to item attributes. Using Amazon customer reviews in the book category, we have confirmed that our method is able to extract patterns faster than the conventional method, and is better able to exclude uninteresting patterns while retaining the patterns of interest.
{"title":"Text Mining using PrefixSpan constrained by Item Interval and Item Attribute","authors":"Issei Sato, Yu Hirate, H. Yamana","doi":"10.1109/ICDEW.2006.142","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.142","url":null,"abstract":"Applying conventional sequential pattern mining methods to text data extracts many uninteresting patterns, which increases the time to interpret the extracted patterns. To solve this problem, we propose a new sequential pattern mining algorithm by adopting the following two constraints. One is to select sequences with regard to item intervals--the number of items between any two adjacent items in a sequence--and the other is to select sequences with regard to item attributes. Using Amazon customer reviews in the book category, we have confirmed that our method is able to extract patterns faster than the conventional method, and is better able to exclude uninteresting patterns while retaining the patterns of interest.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122877670","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}
The wide spread of location-based services results in a strong market for location-detection devices (e.g., GPS-like devices, RFIDs, handheld devices, and cellular phones). Examples of location-based services include location-aware emergency service, location-based advertisement, live traffic reports, and location-based store finder. However, location-detection devices pose a major privacy threat on its users where it transmits private information (i.e., the location) to the server who may be untrustworthy. The existing model of location-based applications trades service with privacy where if a user wants to keep her private location information, she has to turn off her location-detection device, i.e., unsubscribe from the service. This paper tackles this model in a way that protects the user privacy while keeping the functionality of location-based services. The main idea is to employ a trusted third party, the Location Anonymizer, that expands the user location into a spatial region such that: (1) The exact user location can lie anywhere in the spatial region, and (2) There are k other users within the expanded spatial region so that each user is k-anonymous. The location-based database server is equipped with additional functionalities that support spatio-temporal queries based on the spatial region received from the location anonymizer rather than the exact point location received from the user.
{"title":"Towards Privacy-Aware Location-Based Database Servers","authors":"M. Mokbel","doi":"10.1109/ICDEW.2006.152","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.152","url":null,"abstract":"The wide spread of location-based services results in a strong market for location-detection devices (e.g., GPS-like devices, RFIDs, handheld devices, and cellular phones). Examples of location-based services include location-aware emergency service, location-based advertisement, live traffic reports, and location-based store finder. However, location-detection devices pose a major privacy threat on its users where it transmits private information (i.e., the location) to the server who may be untrustworthy. The existing model of location-based applications trades service with privacy where if a user wants to keep her private location information, she has to turn off her location-detection device, i.e., unsubscribe from the service. This paper tackles this model in a way that protects the user privacy while keeping the functionality of location-based services. The main idea is to employ a trusted third party, the Location Anonymizer, that expands the user location into a spatial region such that: (1) The exact user location can lie anywhere in the spatial region, and (2) There are k other users within the expanded spatial region so that each user is k-anonymous. The location-based database server is equipped with additional functionalities that support spatio-temporal queries based on the spatial region received from the location anonymizer rather than the exact point location received from the user.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125082480","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}
Data mining is the process of discovering hidden and meaningful knowledge in a data set. It has been successfully applied to many real-life problems, for instance, web personalization, network intrusion detection, and customized marketing. Recent advances in computational sciences have led to the application of data mining to various scientific domains, such as astronomy and bioinformatics, to facilitate the understanding of different scientific processes in the underlying domain. In this thesis work, we focus on designing and applying data mining techniques to analyze spatial and spatiotemporal data originated in scientific domains. Examples of spatial and spatio-temporal data in scientific domains include data describing protein structures and data produced from protein folding simulations, respectively. Specifically, we have proposed a generalized framework to effectively discover different types of spatial and spatio-temporal patterns in scientific data sets. Such patterns can be used to capture a variety of interactions among objects of interest and the evolutionary behavior of such interactions. We have applied the framework to analyze data originated in the following three application domains: bioinformatics, computational molecular dynamics, and computational fluid dynamics. Empirical results demonstrate that the discovered patterns are meaningful in the underlying domain and can provide important insights into various scientific phenomena.
{"title":"Mining Spatial and Spatio-Temporal Patterns in Scientific Data","authors":"Hui Yang, S. Parthasarathy","doi":"10.1109/ICDEW.2006.92","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.92","url":null,"abstract":"Data mining is the process of discovering hidden and meaningful knowledge in a data set. It has been successfully applied to many real-life problems, for instance, web personalization, network intrusion detection, and customized marketing. Recent advances in computational sciences have led to the application of data mining to various scientific domains, such as astronomy and bioinformatics, to facilitate the understanding of different scientific processes in the underlying domain. In this thesis work, we focus on designing and applying data mining techniques to analyze spatial and spatiotemporal data originated in scientific domains. Examples of spatial and spatio-temporal data in scientific domains include data describing protein structures and data produced from protein folding simulations, respectively. Specifically, we have proposed a generalized framework to effectively discover different types of spatial and spatio-temporal patterns in scientific data sets. Such patterns can be used to capture a variety of interactions among objects of interest and the evolutionary behavior of such interactions. We have applied the framework to analyze data originated in the following three application domains: bioinformatics, computational molecular dynamics, and computational fluid dynamics. Empirical results demonstrate that the discovered patterns are meaningful in the underlying domain and can provide important insights into various scientific phenomena.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128624882","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}
An appropriate database language characteristics leading to the success of declarative query processing - and, in turn, to the rise of relational DBMSs in general - always provides more than one way of evaluating a query. This counts for structurally different but logically equivalent query evaluation plans (QEPs) as well as for different implementations of the same logical operator. This principle surely holds for the novel XML database management systems (XDBMSs): Recently proposed operators for XML query processing can be grouped into the logical operators Structural Join [1, 22] and Holistic Twig Join [3, 6, 16]. Depending on available internal system mechanisms, a lot of opportunities exist how to implement these operators (two of which are presented in this paper.
{"title":"Twig Query Processing Under Concurrent Updates","authors":"Christian Mathis, T. Härder","doi":"10.1109/ICDEW.2006.156","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.156","url":null,"abstract":"An appropriate database language characteristics leading to the success of declarative query processing - and, in turn, to the rise of relational DBMSs in general - always provides more than one way of evaluating a query. This counts for structurally different but logically equivalent query evaluation plans (QEPs) as well as for different implementations of the same logical operator. This principle surely holds for the novel XML database management systems (XDBMSs): Recently proposed operators for XML query processing can be grouped into the logical operators Structural Join [1, 22] and Holistic Twig Join [3, 6, 16]. Depending on available internal system mechanisms, a lot of opportunities exist how to implement these operators (two of which are presented in this paper.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127241066","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}
Overload management has been an important problem for large-scale dynamic systems. In this paper, we study this problem in the context of our Borealis distributed stream processing system. We show that server nodes must coordinate in their load shedding decisions to achieve global control on output quality. We describe a distributed load shedding approach which provides this coordination by upstream metadata aggregation and propagation. Metadata enables an upstream node to make fast local load shedding decisions which will influence its descendant nodes in the best possible way.
{"title":"Dealing with Overload in Distributed Stream Processing Systems","authors":"Nesime Tatbul, S. Zdonik","doi":"10.1109/ICDEW.2006.45","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.45","url":null,"abstract":"Overload management has been an important problem for large-scale dynamic systems. In this paper, we study this problem in the context of our Borealis distributed stream processing system. We show that server nodes must coordinate in their load shedding decisions to achieve global control on output quality. We describe a distributed load shedding approach which provides this coordination by upstream metadata aggregation and propagation. Metadata enables an upstream node to make fast local load shedding decisions which will influence its descendant nodes in the best possible way.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114784442","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}
P. Mork, A. Rosenthal, Leonard J. Seligman, Joel Korb, Ken Samuel
A key aspect of any data integration endeavor is establishing a transformation that translates instances of one or more source schemata into instances of a target schema. This schema integration task must be tackled regardless of the integration architecture or mapping formalism. In this paper we provide a task model for schema integration. We use this breakdown to motivate a workbench for schema integration in which multiple tools share a common knowledge repository. In particular, the workbench facilitates the interoperation of research prototypes for schema matching (which automatically identify likely semantic correspondences) with commercial schema mapping tools (which help produce instance-level transformations). Currently, each of these tools provides its own ad hoc representation of schemata and mappings; combining these tools requires aligning these representations. The workbench provides a common representation so that these tools can more rapidly be combined.
{"title":"Integration Workbench: Integrating Schema Integration Tools","authors":"P. Mork, A. Rosenthal, Leonard J. Seligman, Joel Korb, Ken Samuel","doi":"10.1109/ICDEW.2006.69","DOIUrl":"https://doi.org/10.1109/ICDEW.2006.69","url":null,"abstract":"A key aspect of any data integration endeavor is establishing a transformation that translates instances of one or more source schemata into instances of a target schema. This schema integration task must be tackled regardless of the integration architecture or mapping formalism. In this paper we provide a task model for schema integration. We use this breakdown to motivate a workbench for schema integration in which multiple tools share a common knowledge repository. In particular, the workbench facilitates the interoperation of research prototypes for schema matching (which automatically identify likely semantic correspondences) with commercial schema mapping tools (which help produce instance-level transformations). Currently, each of these tools provides its own ad hoc representation of schemata and mappings; combining these tools requires aligning these representations. The workbench provides a common representation so that these tools can more rapidly be combined.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129312795","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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