We consider the enumeration of MSO queries over strings under updates. For each MSO query we build an index structure enjoying the following properties: The index structure can be constructed in linear time, it can be updated in logarithmic time and it allows for constant delay time enumeration. This improves from the previous known index structures allowing for constant delay enumeration that would need to be reconstructed from scratch, hence in linear time, in the presence of updates. We allow relabeling updates, insertion of individual labels and removal of individual labels.
{"title":"Enumeration of MSO Queries on Strings with Constant Delay and Logarithmic Updates","authors":"Matthias Niewerth, L. Segoufin","doi":"10.1145/3196959.3196961","DOIUrl":"https://doi.org/10.1145/3196959.3196961","url":null,"abstract":"We consider the enumeration of MSO queries over strings under updates. For each MSO query we build an index structure enjoying the following properties: The index structure can be constructed in linear time, it can be updated in logarithmic time and it allows for constant delay time enumeration. This improves from the previous known index structures allowing for constant delay enumeration that would need to be reconstructed from scratch, hence in linear time, in the presence of updates. We allow relabeling updates, insertion of individual labels and removal of individual labels.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114897135","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 2007, the PODS Executive Committee established a Test-ofTime Award, named after the late Alberto O. Mendelzon, in recognition of his scientific legacy and his service and dedication to the database community. Mendelzon was an international leader in database theory, whose pioneering and fundamental work has inspired and influenced both database theoreticians and practitioners, and continues to be applied in a variety of advanced settings. He served the database community in many ways: he served as both the Program and the General Chair of the PODS conference, and was instrumental in bringing SIGMOD and PODS together. He was an outstanding educator, who guided the research of numerous doctoral students and postdoctoral fellows. The Award is to be given each year to a paper or a small number of papers published in the PODS proceedings ten years prior, that had the most impact (in terms of research, methodology, or transfer of practice) over the intervening decade. The decision was approved by SIGMOD and ACM. The funds for the Award were contributed by IBM Toronto. The PODS Executive Committee has appointed us to serve as the Award Committee for 2018. After careful consideration and having solicited external nominations and advice, we have selected the following paper as the award winner for 2018: “The Chase Revisited" by Alin Deutsch, Alan Nash and Jeff Remmel. Citation. The chase procedure, introduced in the '70s, is a famous technique in the field and has been proved to be important and effective in providing solutions to several problems related to reasoning on data. The paper revisits the standard chase procedure, studying its properties and applicability to classical database problems. Beside settling the open problem of decidability of termination of the standard chase, it investigates the adequacy of the standard chase for a number of data-oriented tasks. The conceptual insight provided by the paper and the technical results presented go much deeper than the modest title of the paper may suggest. They have had a huge impact on the research work carried out in several topics of data management and knowledge bases, including checking query containment under constraints, constraint implication, computing certain answers in data exchange and data integration, query answering in Datalog and its extensions, and ontology-based data access. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. PODS'18, June 10–15, 2018, Hous
{"title":"2018 ACM PODS Alberto O. Mendelzon Test-of-Time Award","authors":"M. Lenzerini, W. Martens, Nicole Schweikardt","doi":"10.1145/3196959.3196993","DOIUrl":"https://doi.org/10.1145/3196959.3196993","url":null,"abstract":"In 2007, the PODS Executive Committee established a Test-ofTime Award, named after the late Alberto O. Mendelzon, in recognition of his scientific legacy and his service and dedication to the database community. Mendelzon was an international leader in database theory, whose pioneering and fundamental work has inspired and influenced both database theoreticians and practitioners, and continues to be applied in a variety of advanced settings. He served the database community in many ways: he served as both the Program and the General Chair of the PODS conference, and was instrumental in bringing SIGMOD and PODS together. He was an outstanding educator, who guided the research of numerous doctoral students and postdoctoral fellows. The Award is to be given each year to a paper or a small number of papers published in the PODS proceedings ten years prior, that had the most impact (in terms of research, methodology, or transfer of practice) over the intervening decade. The decision was approved by SIGMOD and ACM. The funds for the Award were contributed by IBM Toronto. The PODS Executive Committee has appointed us to serve as the Award Committee for 2018. After careful consideration and having solicited external nominations and advice, we have selected the following paper as the award winner for 2018: “The Chase Revisited\" by Alin Deutsch, Alan Nash and Jeff Remmel. Citation. The chase procedure, introduced in the '70s, is a famous technique in the field and has been proved to be important and effective in providing solutions to several problems related to reasoning on data. The paper revisits the standard chase procedure, studying its properties and applicability to classical database problems. Beside settling the open problem of decidability of termination of the standard chase, it investigates the adequacy of the standard chase for a number of data-oriented tasks. The conceptual insight provided by the paper and the technical results presented go much deeper than the modest title of the paper may suggest. They have had a huge impact on the research work carried out in several topics of data management and knowledge bases, including checking query containment under constraints, constraint implication, computing certain answers in data exchange and data integration, query answering in Datalog and its extensions, and ontology-based data access. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. PODS'18, June 10–15, 2018, Hous","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134558759","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 this paper we study how to perform distinct sampling in the streaming model where data contain near-duplicates. The goal of distinct sampling is to return a distinct element uniformly at random from the universe of elements, given that all the near-duplicates are treated as the same element. We also extend the result to the sliding window cases in which we are only interested in the most recent items. We present algorithms with provable theoretical guarantees for datasets in the Euclidean space, and also verify their effectiveness via an extensive set of experiments.
{"title":"Distinct Sampling on Streaming Data with Near-Duplicates","authors":"Jiecao Chen, Qin Zhang","doi":"10.1145/3196959.3196978","DOIUrl":"https://doi.org/10.1145/3196959.3196978","url":null,"abstract":"In this paper we study how to perform distinct sampling in the streaming model where data contain near-duplicates. The goal of distinct sampling is to return a distinct element uniformly at random from the universe of elements, given that all the near-duplicates are treated as the same element. We also extend the result to the sliding window cases in which we are only interested in the most recent items. We present algorithms with provable theoretical guarantees for datasets in the Euclidean space, and also verify their effectiveness via an extensive set of experiments.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133799694","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 last few decades have seen vast progress in computational reasoning. This has included significant developments in theory, increasing maturity of tools both in performance and usability, and the evolution of standards and benchmarks. The purpose of this article is to reflect on the use of reasoning for rewriting and simplifying relational database queries. We undertake a review of some of the results and reasoning algorithms that have been developed with a motivation from query evaluation, and add to this a look at open problems in the area as well as a critique of prior work from the point of view of practice.
{"title":"How Can Reasoners Simplify Database Querying (And Why Haven't They Done It Yet)?","authors":"Michael Benedikt","doi":"10.1145/3196959.3196989","DOIUrl":"https://doi.org/10.1145/3196959.3196989","url":null,"abstract":"The last few decades have seen vast progress in computational reasoning. This has included significant developments in theory, increasing maturity of tools both in performance and usability, and the evolution of standards and benchmarks. The purpose of this article is to reflect on the use of reasoning for rewriting and simplifying relational database queries. We undertake a review of some of the results and reasoning algorithms that have been developed with a motivation from query evaluation, and add to this a look at open problems in the area as well as a critique of prior work from the point of view of practice.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"107 S118","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132904990","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}
We consider statistical estimations of a matrix product over the integers in a distributed setting, where we have two parties Alice and Bob; Alice holds a matrix A and Bob holds a matrix B, and they want to estimate statistics of $A cdot B$. We focus on the well-studied $ell_p$-norm, distinct elements ($p = 0$), $ell_0$-sampling, and heavy hitter problems. The goal is to minimize both the communication cost and the number of rounds of communication. This problem is closely related to the fundamental set-intersection join problem in databases: when $p = 0$ the problem corresponds to the size of the set-intersection join. When $p = ınfty$ the output is simply the pair of sets with the maximum intersection size. When $p = 1$ the problem corresponds to the size of the corresponding natural join. We also consider the heavy hitters problem which corresponds to finding the pairs of sets with intersection size above a certain threshold, and the problem of sampling an intersecting pair of sets uniformly at random.
{"title":"Distributed Statistical Estimation of Matrix Products with Applications","authors":"David P. Woodruff, Qin Zhang","doi":"10.1145/3196959.3196964","DOIUrl":"https://doi.org/10.1145/3196959.3196964","url":null,"abstract":"We consider statistical estimations of a matrix product over the integers in a distributed setting, where we have two parties Alice and Bob; Alice holds a matrix A and Bob holds a matrix B, and they want to estimate statistics of $A cdot B$. We focus on the well-studied $ell_p$-norm, distinct elements ($p = 0$), $ell_0$-sampling, and heavy hitter problems. The goal is to minimize both the communication cost and the number of rounds of communication. This problem is closely related to the fundamental set-intersection join problem in databases: when $p = 0$ the problem corresponds to the size of the set-intersection join. When $p = ınfty$ the output is simply the pair of sets with the maximum intersection size. When $p = 1$ the problem corresponds to the size of the corresponding natural join. We also consider the heavy hitters problem which corresponds to finding the pairs of sets with intersection size above a certain threshold, and the problem of sampling an intersecting pair of sets uniformly at random.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133456260","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}
Consistent query answering (CQA) aims to find meaningful answers to queries when databases are inconsistent, i.e., do not conform to their specifications. Such answers must be certainly true in all repairs, which are consistent databases whose difference from the inconsistent one is minimal, according to some measure. This task is often computationally intractable, and much of CQA research concentrated on finding islands of tractability. Nevertheless, there are many relevant queries for which no efficient solutions exist, which is reflected by the limited practical applicability of the CQA approach. To remedy this, one needs to devise a new CQA framework that provides explicit guarantees on the quality of query answers. However, the standard notions of repair and certain answers are too coarse to permit more elaborate schemes of query answering. Our goal is to provide a new framework for CQA based on revised definitions of repairs and query answering that opens up the possibility of efficient approximate query answering with explicit guarantees. The key idea is to replace the current declarative definition of a repair with an operational one, which explains how a repair is constructed, and how likely it is that a consistent instance is a repair. This allows us to define how certain we are that a tuple should be in the answer. Using this approach, we study the complexity of both exact and approximate CQA. Even though some of the problems remain hard, for many common classes of constraints we can provide meaningful answers in reasonable time, for queries going far beyond the standard CQA approach.
{"title":"An Operational Approach to Consistent Query Answering","authors":"M. Calautti, L. Libkin, Andreas Pieris","doi":"10.1145/3196959.3196966","DOIUrl":"https://doi.org/10.1145/3196959.3196966","url":null,"abstract":"Consistent query answering (CQA) aims to find meaningful answers to queries when databases are inconsistent, i.e., do not conform to their specifications. Such answers must be certainly true in all repairs, which are consistent databases whose difference from the inconsistent one is minimal, according to some measure. This task is often computationally intractable, and much of CQA research concentrated on finding islands of tractability. Nevertheless, there are many relevant queries for which no efficient solutions exist, which is reflected by the limited practical applicability of the CQA approach. To remedy this, one needs to devise a new CQA framework that provides explicit guarantees on the quality of query answers. However, the standard notions of repair and certain answers are too coarse to permit more elaborate schemes of query answering. Our goal is to provide a new framework for CQA based on revised definitions of repairs and query answering that opens up the possibility of efficient approximate query answering with explicit guarantees. The key idea is to replace the current declarative definition of a repair with an operational one, which explains how a repair is constructed, and how likely it is that a consistent instance is a repair. This allows us to define how certain we are that a tuple should be in the answer. Using this approach, we study the complexity of both exact and approximate CQA. Even though some of the problems remain hard, for many common classes of constraints we can provide meaningful answers in reasonable time, for queries going far beyond the standard CQA approach.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123771240","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}
Schema mappings are syntactic specifications of the relationship between two database schemas, typically called the source schema and the target schema. They have been used extensively in formalizing and analyzing data inter-operability tasks, especially data exchange and data integration. There is a growing body of research on deriving schema mappings from data examples, that is, pairs of source and target instances that depict the behavior of the unknown schema mapping. One of the approaches used in this endeavor casts the derivation of a schema mapping from data examples as a learning problem. Earlier work has shown that GAV mappings (global-as-view schema mappings) are learnable in Angluin's model of exact learning with membership queries and equivalence queries. Here, we validate the practical applicability of this theoretical result by designing and implementing an active learning algorithm, called GAV-Learn that derives a syntactic specification of a GAV mapping from a given set of data examples and from a "black-box" implementation. We analyze the properties of GAV-Learn and, among other results, we show that it produces a GAV mapping that has minimal size and is a good approximation of the unknown GAV mapping. Furthermore, we carry out a detailed experimental evaluation that demonstrates the effectiveness of GAV-Learn along different metrics. In particular, we compare GAV-Learn with two earlier approaches for deriving GAV mappings from data examples, and establish that it performs significantly better than the two baselines.
{"title":"Active Learning of GAV Schema Mappings","authors":"B. T. Cate, Phokion G. Kolaitis, Kun Qian, W. Tan","doi":"10.1145/3196959.3196974","DOIUrl":"https://doi.org/10.1145/3196959.3196974","url":null,"abstract":"Schema mappings are syntactic specifications of the relationship between two database schemas, typically called the source schema and the target schema. They have been used extensively in formalizing and analyzing data inter-operability tasks, especially data exchange and data integration. There is a growing body of research on deriving schema mappings from data examples, that is, pairs of source and target instances that depict the behavior of the unknown schema mapping. One of the approaches used in this endeavor casts the derivation of a schema mapping from data examples as a learning problem. Earlier work has shown that GAV mappings (global-as-view schema mappings) are learnable in Angluin's model of exact learning with membership queries and equivalence queries. Here, we validate the practical applicability of this theoretical result by designing and implementing an active learning algorithm, called GAV-Learn that derives a syntactic specification of a GAV mapping from a given set of data examples and from a \"black-box\" implementation. We analyze the properties of GAV-Learn and, among other results, we show that it produces a GAV mapping that has minimal size and is a good approximation of the unknown GAV mapping. Furthermore, we carry out a detailed experimental evaluation that demonstrates the effectiveness of GAV-Learn along different metrics. In particular, we compare GAV-Learn with two earlier approaches for deriving GAV mappings from data examples, and establish that it performs significantly better than the two baselines.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131894816","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}
We pursue an investigation of data-driven collaborative workflows. In the model, peers can access and update local data, causing side-effects on other peers' data. In this paper, we study means of explaining to a peer her local view of a global run, both at runtime and statically. We consider the notion of "scenario for a given peer" that is a subrun observationally equivalent to the original run for that peer. Because such a scenario can sometimes differ significantly from what happens in the actual run, thus providing a misleading explanation, we introduce and study a faithfulness requirement that ensures closer adherence to the global run. We show that there is a unique minimal faithful scenario, that explains what is happening in the global run by extracting only the portion relevant to the peer. With regard to static explanations, we consider the problem of synthesizing, for each peer, a "view program" whose runs generate exactly the peer's observations of the global runs. Assuming some conditions desirable in their own right, namely transparency and boundedness, we show that such a view program exists and can be synthesized. As an added benefit, the view program rules provide provenance information for the updates observed by the peer.
{"title":"Explanations and Transparency in Collaborative Workflows","authors":"S. Abiteboul, P. Bourhis, V. Vianu","doi":"10.1145/3196959.3196975","DOIUrl":"https://doi.org/10.1145/3196959.3196975","url":null,"abstract":"We pursue an investigation of data-driven collaborative workflows. In the model, peers can access and update local data, causing side-effects on other peers' data. In this paper, we study means of explaining to a peer her local view of a global run, both at runtime and statically. We consider the notion of \"scenario for a given peer\" that is a subrun observationally equivalent to the original run for that peer. Because such a scenario can sometimes differ significantly from what happens in the actual run, thus providing a misleading explanation, we introduce and study a faithfulness requirement that ensures closer adherence to the global run. We show that there is a unique minimal faithful scenario, that explains what is happening in the global run by extracting only the portion relevant to the peer. With regard to static explanations, we consider the problem of synthesizing, for each peer, a \"view program\" whose runs generate exactly the peer's observations of the global runs. Assuming some conditions desirable in their own right, namely transparency and boundedness, we show that such a view program exists and can be synthesized. As an added benefit, the view program rules provide provenance information for the updates observed by the peer.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128959188","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. Agarwal, K. Fox, Kamesh Munagala, Abhinandan Nath, Jiangwei Pan, Erin Taylor
We propose a model for subtrajectory clustering ---the clustering of subsequences of trajectories; each cluster of subtrajectories is represented as a pathlet, a sequence of points that is not necessarily a subsequence of an input trajectory. Given a set of trajectories, our clustering model attempts to capture the shared portions between them by assuming each trajectory is a concatenation of a small set of pathlets, with possible gaps in between. We present a single objective function for finding the optimal collection of pathlets that best represents the trajectories taking into account noise and other artifacts of the data. We show that the subtrajectory clustering problem is NP-Hard and present fast approximation algorithms for subtrajectory clustering. We further improve the running time of our algorithm if the input trajectories are "well-behaved." Finally, we present experimental results on both real and synthetic data sets. We show via visualization and quantitative analysis that the algorithm indeed handles the desiderata of being robust to variations, being efficient and accurate, and being data-driven.
{"title":"Subtrajectory Clustering: Models and Algorithms","authors":"P. Agarwal, K. Fox, Kamesh Munagala, Abhinandan Nath, Jiangwei Pan, Erin Taylor","doi":"10.1145/3196959.3196972","DOIUrl":"https://doi.org/10.1145/3196959.3196972","url":null,"abstract":"We propose a model for subtrajectory clustering ---the clustering of subsequences of trajectories; each cluster of subtrajectories is represented as a pathlet, a sequence of points that is not necessarily a subsequence of an input trajectory. Given a set of trajectories, our clustering model attempts to capture the shared portions between them by assuming each trajectory is a concatenation of a small set of pathlets, with possible gaps in between. We present a single objective function for finding the optimal collection of pathlets that best represents the trajectories taking into account noise and other artifacts of the data. We show that the subtrajectory clustering problem is NP-Hard and present fast approximation algorithms for subtrajectory clustering. We further improve the running time of our algorithm if the input trajectories are \"well-behaved.\" Finally, we present experimental results on both real and synthetic data sets. We show via visualization and quantitative analysis that the algorithm indeed handles the desiderata of being robust to variations, being efficient and accurate, and being data-driven.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132212021","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 recent years, the field of succinct data structures (SDS) has grown rapidly. SDS store data in main memory space that approaches an information-theoretic minimum, and support operations on the data with little or no slow-down compared to their conventional counterparts. In practice, an SDS uses one to two orders of magnitude less main memory than a conventional data structure. For this reason, SDS are becoming a popular approach for storing data that is only somewhat bigger than main memory. This tutorial explores the fundamentals of SDS and their applications to a variety of database problems.
{"title":"In-memory Representations of Databases via Succinct Data Structures: Tutorial Abstract","authors":"R. Raman","doi":"10.1145/3196959.3196992","DOIUrl":"https://doi.org/10.1145/3196959.3196992","url":null,"abstract":"In recent years, the field of succinct data structures (SDS) has grown rapidly. SDS store data in main memory space that approaches an information-theoretic minimum, and support operations on the data with little or no slow-down compared to their conventional counterparts. In practice, an SDS uses one to two orders of magnitude less main memory than a conventional data structure. For this reason, SDS are becoming a popular approach for storing data that is only somewhat bigger than main memory. This tutorial explores the fundamentals of SDS and their applications to a variety of database problems.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"43 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126117887","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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