Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems最新文献
Computing power has been growing steadily, just as communication rate and memory size. Simultaneously our ability to create data has been growing phenomenally and therefore the need to analyze it. We now have examples of massive data streams that are created in far higher rate than we can capture and store in memory economically, gathered in far more quantity than can be transported to central databases without overwhelming the communication infrastructure, and arrives far faster than we can compute with them in a sophisticated way. This phenomenon has challenged how we store, communicate and compute with data. Theories developed over past 50 years have relied on full capture, storage and communication of data. Instead, what we need for managing modern massive data streams are new methods built around working with less. The past 10 years have seen new theories emerge in computing (data stream algorithms), communication (compressed sensing), databases (data stream management systems) and other areas to address the challenges of massive data streams. Still, lot remains open and new applications of massive data streams have emerged recently. We present an overview of these challenges.
{"title":"Theory of data stream computing: where to go","authors":"S. Muthukrishnan","doi":"10.1145/1989284.1989314","DOIUrl":"https://doi.org/10.1145/1989284.1989314","url":null,"abstract":"Computing power has been growing steadily, just as communication rate and memory size. Simultaneously our ability to create data has been growing phenomenally and therefore the need to analyze it. We now have examples of massive data streams that are created in far higher rate than we can capture and store in memory economically, gathered in far more quantity than can be transported to central databases without overwhelming the communication infrastructure, and arrives far faster than we can compute with them in a sophisticated way.\u0000 This phenomenon has challenged how we store, communicate and compute with data. Theories developed over past 50 years have relied on full capture, storage and communication of data. Instead, what we need for managing modern massive data streams are new methods built around working with less. The past 10 years have seen new theories emerge in computing (data stream algorithms), communication (compressed sensing), databases (data stream management systems) and other areas to address the challenges of massive data streams. Still, lot remains open and new applications of massive data streams have emerged recently. We present an overview of these challenges.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"9 1","pages":"317-319"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75803152","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}
While XML is nowadays adopted as the de facto standard for data exchange, historically, its predecessor SGML was invented for describing electronic documents, i.e., marked up text. Actually, today there are still large volumes of such XML texts. We consider simple transformations which can change the internal structure of documents, that is, the mark-up, and can filter out parts of the text but do not disrupt the ordering of the words. Specifically, we focus on XML transformations where the transformed document is a subsequence of the input document when ignoring mark-up. We call the latter text-preserving XML transformations. We characterize such transformations as copy- and rearrange-free transductions. Furthermore, we study the problem of deciding whether a given XML transducer is text-preserving over a given tree language. We consider top-down transducers as well as the abstraction of XSLT called DTL. We show that deciding whether a transformation is text-preserving over an unranked regular tree language is in PTime for top-down transducers, EXPTime-complete for DTL with XPath, and decidable for DTL with MSO patterns. Finally, we obtain that for every transducer in one of the above mentioned classes, the maximal subset of the input schema can be computed on which the transformation is text-preserving.
{"title":"The complexity of text-preserving XML transformations","authors":"Timos Antonopoulos, W. Martens, F. Neven","doi":"10.1145/1989284.1989316","DOIUrl":"https://doi.org/10.1145/1989284.1989316","url":null,"abstract":"While XML is nowadays adopted as the de facto standard for data exchange, historically, its predecessor SGML was invented for describing electronic documents, i.e., marked up text. Actually, today there are still large volumes of such XML texts. We consider simple transformations which can change the internal structure of documents, that is, the mark-up, and can filter out parts of the text but do not disrupt the ordering of the words. Specifically, we focus on XML transformations where the transformed document is a subsequence of the input document when ignoring mark-up. We call the latter text-preserving XML transformations. We characterize such transformations as copy- and rearrange-free transductions. Furthermore, we study the problem of deciding whether a given XML transducer is text-preserving over a given tree language. We consider top-down transducers as well as the abstraction of XSLT called DTL. We show that deciding whether a transformation is text-preserving over an unranked regular tree language is in PTime for top-down transducers, EXPTime-complete for DTL with XPath, and decidable for DTL with MSO patterns. Finally, we obtain that for every transducer in one of the above mentioned classes, the maximal subset of the input schema can be computed on which the transformation is text-preserving.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"19 1","pages":"247-258"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73163603","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 availability of large data centers with tens of thousands of servers has led to the popular adoption of massive parallelism for data analysis on large datasets. Several query languages exist for running queries on massively parallel architectures, some based on the MapReduce infrastructure, others using proprietary implementations. Motivated by this trend, this paper analyzes the parallel complexity of conjunctive queries. We propose a very simple model of parallel computation that captures these architectures, in which the complexity parameter is the number of parallel steps requiring synchronization of all servers. We study the complexity of conjunctive queries and give a complete characterization of the queries which can be computed in one parallel step. These form a strict subset of hierarchical queries, and include flat queries like R(x,y), S(x,z), T(x,v), U(x,w), tall queries like R(x), S(x,y), T(x,y,z), U(x,y,z,w), and combinations thereof, which we call tall-flat queries. We describe an algorithm for computing in parallel any tall-flat query, and prove that any query that is not tall-flat cannot be computed in one step in this model. Finally, we present extensions of our results to queries that are not tall-flat.
{"title":"Parallel evaluation of conjunctive queries","authors":"Paraschos Koutris, Dan Suciu","doi":"10.1145/1989284.1989310","DOIUrl":"https://doi.org/10.1145/1989284.1989310","url":null,"abstract":"The availability of large data centers with tens of thousands of servers has led to the popular adoption of massive parallelism for data analysis on large datasets. Several query languages exist for running queries on massively parallel architectures, some based on the MapReduce infrastructure, others using proprietary implementations. Motivated by this trend, this paper analyzes the parallel complexity of conjunctive queries. We propose a very simple model of parallel computation that captures these architectures, in which the complexity parameter is the number of parallel steps requiring synchronization of all servers. We study the complexity of conjunctive queries and give a complete characterization of the queries which can be computed in one parallel step. These form a strict subset of hierarchical queries, and include flat queries like R(x,y), S(x,z), T(x,v), U(x,w), tall queries like R(x), S(x,y), T(x,y,z), U(x,y,z,w), and combinations thereof, which we call tall-flat queries. We describe an algorithm for computing in parallel any tall-flat query, and prove that any query that is not tall-flat cannot be computed in one step in this model. Finally, we present extensions of our results to queries that are not tall-flat.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"38 1","pages":"223-234"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1989284.1989310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72469178","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}
Darakhshan J. Mir, S. Muthukrishnan, Aleksandar Nikolov, R. Wright
Consider fully dynamic data, where we track data as it gets inserted and deleted. There are well developed notions of private data analyses with dynamic data, for example, using differential privacy. We want to go beyond privacy, and consider privacy together with security, formulated recently as pan-privacy by Dwork et al. (ICS 2010). Informally, pan-privacy preserves differential privacy while computing desired statistics on the data, even if the internal memory of the algorithm is compromised (say, by a malicious break-in or insider curiosity or by fiat by the government or law). We study pan-private algorithms for basic analyses, like estimating distinct count, moments, and heavy hitter count, with fully dynamic data. We present the first known pan-private algorithms for these problems in the fully dynamic model. Our algorithms rely on sketching techniques popular in streaming: in some cases, we add suitable noise to a previously known sketch, using a novel approach of calibrating noise to the underlying problem structure and the projection matrix of the sketch; in other cases, we maintain certain statistics on sketches; in yet others, we define novel sketches. We also present the first known lower bounds explicitly for pan privacy, showing our results to be nearly optimal for these problems. Our lower bounds are stronger than those implied by differential privacy or dynamic data streaming alone and hold even if unbounded memory and/or unbounded processing time are allowed. The lower bounds use a noisy decoding argument and exploit a connection between pan-private algorithms and data sanitization.
{"title":"Pan-private algorithms via statistics on sketches","authors":"Darakhshan J. Mir, S. Muthukrishnan, Aleksandar Nikolov, R. Wright","doi":"10.1145/1989284.1989290","DOIUrl":"https://doi.org/10.1145/1989284.1989290","url":null,"abstract":"Consider fully dynamic data, where we track data as it gets inserted and deleted. There are well developed notions of private data analyses with dynamic data, for example, using differential privacy. We want to go beyond privacy, and consider privacy together with security, formulated recently as pan-privacy by Dwork et al. (ICS 2010). Informally, pan-privacy preserves differential privacy while computing desired statistics on the data, even if the internal memory of the algorithm is compromised (say, by a malicious break-in or insider curiosity or by fiat by the government or law).\u0000 We study pan-private algorithms for basic analyses, like estimating distinct count, moments, and heavy hitter count, with fully dynamic data. We present the first known pan-private algorithms for these problems in the fully dynamic model. Our algorithms rely on sketching techniques popular in streaming: in some cases, we add suitable noise to a previously known sketch, using a novel approach of calibrating noise to the underlying problem structure and the projection matrix of the sketch; in other cases, we maintain certain statistics on sketches; in yet others, we define novel sketches. We also present the first known lower bounds explicitly for pan privacy, showing our results to be nearly optimal for these problems. Our lower bounds are stronger than those implied by differential privacy or dynamic data streaming alone and hold even if unbounded memory and/or unbounded processing time are allowed. The lower bounds use a noisy decoding argument and exploit a connection between pan-private algorithms and data sanitization.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"2009 1","pages":"37-48"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86246699","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}
In the traditional data exchange setting, source instances are restricted to be complete in the sense that every fact is either true or false in these instances. Although natural for a typical database translation scenario, this restriction is gradually becoming an impediment to the development of a wide range of applications that need to exchange objects that admit several interpretations. In particular, we are motivated by two specific applications that go beyond the usual data exchange scenario: exchanging incomplete information and exchanging knowledge bases. In this paper, we propose a general framework for data exchange that can deal with these two applications. More specifically, we address the problem of exchanging information given by representation systems, which are essentially finite descriptions of (possibly infinite) sets of complete instances. We make use of the classical semantics of mappings specified by sets of logical sentences to give a meaningful semantics to the notion of exchanging representatives, from which the standard notions of solution, space of solutions, and universal solution naturally arise. We also introduce the notion of strong representation system for a class of mappings, that resembles the concept of strong representation system for a query language. We show the robustness of our proposal by applying it to the two applications mentioned above: exchanging incomplete information and exchanging knowledge bases, which are both instantiations of the exchanging problem for representation systems. We study these two applications in detail, presenting results regarding expressiveness, query answering and complexity of computing solutions, and also algorithms to materialize solutions.
{"title":"Data exchange beyond complete data","authors":"M. Arenas, Jorge Pérez, Juan L. Reutter","doi":"10.1145/1989284.1989293","DOIUrl":"https://doi.org/10.1145/1989284.1989293","url":null,"abstract":"In the traditional data exchange setting, source instances are restricted to be complete in the sense that every fact is either true or false in these instances. Although natural for a typical database translation scenario, this restriction is gradually becoming an impediment to the development of a wide range of applications that need to exchange objects that admit several interpretations. In particular, we are motivated by two specific applications that go beyond the usual data exchange scenario: exchanging incomplete information and exchanging knowledge bases. In this paper, we propose a general framework for data exchange that can deal with these two applications. More specifically, we address the problem of exchanging information given by representation systems, which are essentially finite descriptions of (possibly infinite) sets of complete instances. We make use of the classical semantics of mappings specified by sets of logical sentences to give a meaningful semantics to the notion of exchanging representatives, from which the standard notions of solution, space of solutions, and universal solution naturally arise. We also introduce the notion of strong representation system for a class of mappings, that resembles the concept of strong representation system for a query language. We show the robustness of our proposal by applying it to the two applications mentioned above: exchanging incomplete information and exchanging knowledge bases, which are both instantiations of the exchanging problem for representation systems. We study these two applications in detail, presenting results regarding expressiveness, query answering and complexity of computing solutions, and also algorithms to materialize solutions.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"340 1","pages":"83-94"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87806300","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}
Database queries can be broadly classified into two categories: reporting queries and aggregation queries. The former retrieves a collection of records from the database that match the query's conditions, while the latter returns an aggregate, such as count, sum, average, or max (min), of a particular attribute of these records. Aggregation queries are especially useful in business intelligence and data analysis applications where users are interested not in the actual records, but some statistics of them. They can also be executed much more efficiently than reporting queries, by embedding properly precomputed aggregates into an index. However, reporting and aggregation queries provide only two extremes for exploring the data. Data analysts often need more insight into the data distribution than what those simple aggregates provide, and yet certainly do not want the sheer volume of data returned by reporting queries. In this paper, we design indexing techniques that allow for extracting a statistical summary of all the records in the query. The summaries we support include frequent items, quantiles, various sketches, and wavelets, all of which are of central importance in massive data analysis. Our indexes require linear space and extract a summary with the optimal or near-optimal query cost.
{"title":"Beyond simple aggregates: indexing for summary queries","authors":"Zhewei Wei, K. Yi","doi":"10.1145/1989284.1989299","DOIUrl":"https://doi.org/10.1145/1989284.1989299","url":null,"abstract":"Database queries can be broadly classified into two categories: reporting queries and aggregation queries. The former retrieves a collection of records from the database that match the query's conditions, while the latter returns an aggregate, such as count, sum, average, or max (min), of a particular attribute of these records. Aggregation queries are especially useful in business intelligence and data analysis applications where users are interested not in the actual records, but some statistics of them. They can also be executed much more efficiently than reporting queries, by embedding properly precomputed aggregates into an index.\u0000 However, reporting and aggregation queries provide only two extremes for exploring the data. Data analysts often need more insight into the data distribution than what those simple aggregates provide, and yet certainly do not want the sheer volume of data returned by reporting queries. In this paper, we design indexing techniques that allow for extracting a statistical summary of all the records in the query. The summaries we support include frequent items, quantiles, various sketches, and wavelets, all of which are of central importance in massive data analysis. Our indexes require linear space and extract a summary with the optimal or near-optimal query cost.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"37 1","pages":"117-128"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80545572","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}
Complex Event Processing (CEP) Systems are stream processing systems that monitor incoming event streams in search of userspecified event patterns. While CEP systems have been adopted in a variety of applications, the privacy implications of event pattern reporting mechanisms have yet to be studied - a stark contrast to the significant amount of attention that has been devoted to privacy for relational systems. In this paper we present a privacy problem that arises when the system must support desired patterns (those that should be reported if detected) and private patterns (those that should not be revealed). We formalize this problem, which we term privacy-preserving, utility maximizing CEP (PP-CEP), and analyze its complexity under various assumptions. Our results show that this is a rich problem to study and shed some light on the difficulty of developing algorithms that preserve utility without compromising privacy.
{"title":"On the complexity of privacy-preserving complex event processing","authors":"Yeye He, Siddharth Barman, Di Wang, J. Naughton","doi":"10.1145/1989284.1989304","DOIUrl":"https://doi.org/10.1145/1989284.1989304","url":null,"abstract":"Complex Event Processing (CEP) Systems are stream processing systems that monitor incoming event streams in search of userspecified event patterns. While CEP systems have been adopted in a variety of applications, the privacy implications of event pattern reporting mechanisms have yet to be studied - a stark contrast to the significant amount of attention that has been devoted to privacy for relational systems. In this paper we present a privacy problem that arises when the system must support desired patterns (those that should be reported if detected) and private patterns (those that should not be revealed). We formalize this problem, which we term privacy-preserving, utility maximizing CEP (PP-CEP), and analyze its complexity under various assumptions. Our results show that this is a rich problem to study and shed some light on the difficulty of developing algorithms that preserve utility without compromising privacy.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"61 1","pages":"165-174"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84614415","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}
Consider the situation where a query is to be answered using Web sources that restrict the accesses that can be made on backend relational data by requiring some attributes to be given as input of the service. The accesses provide lookups on the collection of attributes values that match the binding. They can differ in whether or not they require arguments to be generated from prior accesses. Prior work has focused on the question of whether a query can be answered using a set of data sources, and in developing static access plans (e.g., Datalog programs) that implement query answering. We are interested in dynamic aspects of the query answering problem: given partial information about the data, which accesses could provide relevant data for answering a given query? We consider immediate and long-term notions of "relevant accesses", and ascertain the complexity of query relevance, for both conjunctive queries and arbitrary positive queries. In the process, we relate dynamic relevance of an access to query containment under access limitations and characterize the complexity of this problem; we produce several complexity results about containment that are of interest by themselves.
{"title":"Determining relevance of accesses at runtime","authors":"Michael Benedikt, G. Gottlob, P. Senellart","doi":"10.1145/1989284.1989309","DOIUrl":"https://doi.org/10.1145/1989284.1989309","url":null,"abstract":"Consider the situation where a query is to be answered using Web sources that restrict the accesses that can be made on backend relational data by requiring some attributes to be given as input of the service. The accesses provide lookups on the collection of attributes values that match the binding. They can differ in whether or not they require arguments to be generated from prior accesses. Prior work has focused on the question of whether a query can be answered using a set of data sources, and in developing static access plans (e.g., Datalog programs) that implement query answering. We are interested in dynamic aspects of the query answering problem: given partial information about the data, which accesses could provide relevant data for answering a given query? We consider immediate and long-term notions of \"relevant accesses\", and ascertain the complexity of query relevance, for both conjunctive queries and arbitrary positive queries. In the process, we relate dynamic relevance of an access to query containment under access limitations and characterize the complexity of this problem; we produce several complexity results about containment that are of interest by themselves.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"24 1","pages":"211-222"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73901895","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}
Graph data appears in a variety of application domains, and many uses of it, such as querying, matching, and transforming data, naturally result in incompletely specified graph data, i.e., graph patterns. While queries need to be posed against such data, techniques for querying patterns are generally lacking, and properties of such queries are not well understood. Our goal is to study the basics of querying graph patterns. We first identify key features of patterns, such as node and label variables and edges specified by regular expressions, and define a classification of patterns based on them. We then study standard graph queries on graph patterns, and give precise characterizations of both data and combined complexity for each class of patterns. If complexity is high, we do further analysis of features that lead to intractability, as well as lower complexity restrictions. We introduce a new automata model for query answering with two modes of acceptance: one captures queries returning nodes, and the other queries returning paths. We study properties of such automata, and the key computational tasks associated with them. Finally, we provide additional restrictions for tractability, and show that some intractable cases can be naturally cast as instances of constraint satisfaction problem.
{"title":"Querying graph patterns","authors":"P. Barceló, L. Libkin, Juan L. Reutter","doi":"10.1145/1989284.1989307","DOIUrl":"https://doi.org/10.1145/1989284.1989307","url":null,"abstract":"Graph data appears in a variety of application domains, and many uses of it, such as querying, matching, and transforming data, naturally result in incompletely specified graph data, i.e., graph patterns. While queries need to be posed against such data, techniques for querying patterns are generally lacking, and properties of such queries are not well understood.\u0000 Our goal is to study the basics of querying graph patterns. We first identify key features of patterns, such as node and label variables and edges specified by regular expressions, and define a classification of patterns based on them. We then study standard graph queries on graph patterns, and give precise characterizations of both data and combined complexity for each class of patterns. If complexity is high, we do further analysis of features that lead to intractability, as well as lower complexity restrictions. We introduce a new automata model for query answering with two modes of acceptance: one captures queries returning nodes, and the other queries returning paths. We study properties of such automata, and the key computational tasks associated with them. Finally, we provide additional restrictions for tractability, and show that some intractable cases can be naturally cast as instances of constraint satisfaction problem.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"28 1","pages":"199-210"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73827559","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}
This paper studies first-in-first-out (FIFO) indexes, each of which manages a dataset where objects are deleted in the same order as their insertions. We give a technique that converts a static data structure to a FIFO index for all decomposable problems, provided that the static structure can be constructed efficiently. We present FIFO access methods to solve several problems including half-plane search, nearest neighbor search, and extreme-point search. All of our structures consume linear space, and have optimal or near-optimal query cost.
{"title":"FIFO indexes for decomposable problems","authors":"Cheng Sheng, Yufei Tao","doi":"10.1145/1989284.1989291","DOIUrl":"https://doi.org/10.1145/1989284.1989291","url":null,"abstract":"This paper studies first-in-first-out (FIFO) indexes, each of which manages a dataset where objects are deleted in the same order as their insertions. We give a technique that converts a static data structure to a FIFO index for all decomposable problems, provided that the static structure can be constructed efficiently. We present FIFO access methods to solve several problems including half-plane search, nearest neighbor search, and extreme-point search. All of our structures consume linear space, and have optimal or near-optimal query cost.","PeriodicalId":92118,"journal":{"name":"Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems","volume":"78 1","pages":"25-35"},"PeriodicalIF":0.0,"publicationDate":"2011-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74211429","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}
Proceedings of the ... ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems
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