{"title":"The Inverse of a Schema Mapping","authors":"Jorge Pérez","doi":"10.4230/DFU.Vol5.10452.69","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.69","url":null,"abstract":"The inversion of schema mappings has been identified as one of the fundamental operators for the development of a general framework for data exchange, data integration, and more generally, for metadata management. Given a mapping M from a schema S to a schema T, \u0000an inverse of M is a new mapping that describes the reverse relationship fromT to S, and that is semantically consistent with the relationship previously established by M. In practical scenarios, the inversion of a schema mapping can have several applications. \u0000For example, in a data exchange context, if a mapping M is used to exchange data from a source to a target schema, an inverse of M can be used to exchange the data back to the source, thus reversing the application of M. \u0000 \u0000The formalization of a clear semantics for the inverse operator has proved to be a very difficult task. In fact, during the last years, several alternative notions of inversion for schema mappings have been proposed in the literature. This chapter provides a survey on the different formalizations for the inverse operator and the main theoretical and practical results obtained so far. In particular, we present and compare the main proposals for inverting schema mappings that have been considered in the literature. For each one of them we present their formal semantics and characterizations of their existence. We also present algorithms to compute inverses and study the language needed to express such inverses.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127905662","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}
{"title":"Management of Inconsistencies in Data Integration","authors":"Ekaterini Ioannou, S. Staworko","doi":"10.4230/DFU.Vol5.10452.217","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.217","url":null,"abstract":"Data integration aims at providing a unified view over data coming from various sources. One of the most challenging tasks for data integration is handling the inconsistencies that appear in the integrated data in an efficient and effective manner. In this chapter, we provide a survey on techniques introduced for handling inconsistencies in data integration, focusing on two groups. The first group contains techniques for computing consistent query answers, and includes mechanisms for the compact representation of repairs, query rewriting, and logic programs. The second group contains techniques focusing on the resolution of inconsistencies. This includes methodologies for computing similarity between atomic values as well as similarity between groups of data, collective techniques, scaling to large datasets, and dealing with uncertainty that is related to inconsistencies.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121662180","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}
{"title":"Data Stream Management Systems","authors":"Sandra Geisler","doi":"10.4230/DFU.Vol5.10452.275","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.275","url":null,"abstract":"In many application fields, such as production lines or stock analysis, it is substantial to create and process high amounts of data at high rates. Such continuous data flows with unknown size and end are also called data streams. The processing and analysis of data streams are a challenge for common data management systems as they have to operate and deliver results in real time. Data Stream Management Systems (DSMS), as an advancement of database management systems, have been implemented to deal with these issues. DSMS have to adapt to the notion of data streams on various levels, such as query languages, processing or optimization. In this chapter we give an overview of the basics of data streams, architecture principles of DSMS and the used query languages. Furthermore, we specifically detail data quality aspects in DSMS as these play an important role for various applications based on data streams. Finally, the chapter also includes a list of research and commercial DSMS and their key properties.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129771419","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}
{"title":"Algorithms for Core Computation in Data Exchange","authors":"V. Savenkov","doi":"10.4230/DFU.Vol5.10452.38","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.38","url":null,"abstract":"We describe the state of the art in the area of core computation for data exchange. Two main approaches are considered: post-processing core computation, applied to a canonical universal solution constructed by chasing a given schema mapping, and direct core computation, where the mapping is first rewritten in order to create core universal solutions by chasing it.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"87 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116305585","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}
{"title":"Semantics for Non-Monotone Queries in Data Exchange and Data Integration","authors":"André Hernich","doi":"10.4230/DFU.Vol5.10452.161","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.161","url":null,"abstract":"A fundamental question in data exchange and data integration is how to answer queries that are posed against the target schema, or the global schema, respectively. While the certain answers semantics has proved to be adequate for answering monotone queries, the question concerning an appropriate semantics for non-monotone queries turned out to be more difficult. This article surveys approaches and semantics for answering non-monotone queries in data exchange and data integration.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123222616","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}
{"title":"Query Processing in Data Integration","authors":"P. Guagliardo, Piotr Wieczorek","doi":"10.4230/DFU.Vol5.10452.129","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.129","url":null,"abstract":"In this chapter we illustrate the main techniques for processing queries in data integration. The first part of the chapter focuses on the problem of query answering in the relational setting, and describes approaches based on variants of the chase, along with how to deal with integrity constraints and access patterns. The second part of the chapter investigates query processing in the context of semistructured data, which is best described by graph-based data models, where the expressiveness of query languages not common in traditional database systems allows to point out the subtle differences between query answering and query rewriting. The chapter is closed by a very brief discussion of query processing in data integration with XML and ontologies.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125369017","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}
{"title":"The Chase Procedure and its Applications in Data Exchange","authors":"Adrian Onet","doi":"10.4230/DFU.Vol5.10452.1","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.1","url":null,"abstract":"The initial and basic role of the chase procedure was to test logical implication between sets of dependencies in order to determine equivalence of database instances known to satisfy a given set of dependencies and to determine query equivalence under database constrains. Recently the chase procedure has experienced a revival due to its application in data exchange. In this chapter we review the chase algorithm and its properties as well as its application in data exchange.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263314","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}
{"title":"Reasoning about Schema Mappings","authors":"Emanuel Sallinger","doi":"10.4230/DFU.Vol5.10452.97","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.97","url":null,"abstract":"Schema mappings are an important tool in several areas of database research. Recently, the topic of reasoning about schema mappings was given attention, in particular revolving around the central concepts of equivalence and optimality. In this chapter, we survey these results. First, we introduce relaxed notions of logical equivalence and show their potential for finding optimized schema mappings. We then look at applications of these concepts to optimization, normalization, and schema mapping management, as well as the boundaries of computability. We conclude by giving a glimpse at reasoning about schema mappings in a broader sense by looking at how to debug schema mappings.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133060174","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}
{"title":"Algorithmic Techniques for Processing Data Streams","authors":"E. Ikonomovska, Mariano Zelke","doi":"10.4230/DFU.Vol5.10452.237","DOIUrl":"https://doi.org/10.4230/DFU.Vol5.10452.237","url":null,"abstract":"We give a survey at some algorithmic techniques for processing data streams. After covering the basic methods of sampling and sketching, we present more evolved procedures that resort on those basic ones. In particular, we examine algorithmic schemes for similarity mining, the concept of group testing, and techniques for clustering and summarizing data streams.","PeriodicalId":409733,"journal":{"name":"Data Exchange, Information, and Streams","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131027466","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}
京公网安备 11010802042870号
求助内容：
应助结果提醒方式：