Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems最新文献

{"title":"Distance-Sensitive Hashing","authors":"Martin Aumüller, Tobias Christiani, R. Pagh, Francesco Silvestri","doi":"10.1145/3196959.3196976","DOIUrl":"https://doi.org/10.1145/3196959.3196976","url":null,"abstract":"Locality-sensitive hashing (LSH) is an important tool for managing high-dimensional noisy or uncertain data, for example in connection with data cleaning (similarity join) and noise-robust search (similarity search). However, for a number of problems the LSH framework is not known to yield good solutions, and instead ad hoc solutions have been designed for particular similarity and distance measures. For example, this is true for output-sensitive similarity search/join, and for indexes supporting annulus queries that aim to report a point close to a certain given distance from the query point. In this paper we initiate the study of distance-sensitive hashing (DSH), a generalization of LSH that seeks a family of hash functions such that the probability of two points having the same hash value is a given function of the distance between them. More precisely, given a distance space (X, dist ) and a \"collision probability function\" (CPF) f: R -> [0,1] we seek a distribution over pairs of functions (h,g) such that for every pair of points x, y ın X the collision probability is ¶r[h(x)=g(y)] = f(dist(x,y)). Locality-sensitive hashing is the study of how fast a CPF can decrease as the distance grows. For many spaces, f can be made exponentially decreasing even if we restrict attention to the symmetric case where g=h. We show that the asymmetry achieved by having a pair of functions makes it possible to achieve CPFs that are, for example, increasing or unimodal, and show how this leads to principled solutions to problems not addressed by the LSH framework. This includes a novel application to privacy-preserving distance estimation. We believe that the DSH framework will find further applications in high-dimensional data management. To put the running time bounds of the proposed constructions into perspective, we show lower bounds for the performance of DSH constructions with increasing and decreasing CPFs under angular distance. Essentially, this shows that our constructions are tight up to lower order terms. In particular, we extend existing LSH lower bounds, showing that they also hold in the asymmetric setting.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124402145","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":"In-Database Learning with Sparse Tensors","authors":"Mahmoud Abo Khamis, H. Ngo, X. Nguyen, Dan Olteanu, Maximilian Schleich","doi":"10.1145/3196959.3196960","DOIUrl":"https://doi.org/10.1145/3196959.3196960","url":null,"abstract":"In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features, export the data, convert data format, train models using an external tool, reimport the parameters. It is also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This paper introduces a unified framework for training and evaluating a class of statistical learning models inside a relational database. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from relational database theory such as schema information, query structure, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database learning problems and design efficient algorithms to solve them. The algorithms and models proposed in the paper have already been implemented and deployed in retail-planning and forecasting applications, with significant performance benefits over out-of-database solutions that require the costly data-export loop.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122979250","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":"General and Fractional Hypertree Decompositions: Hard and Easy Cases","authors":"Wolfgang Fischl, G. Gottlob, R. Pichler","doi":"10.1145/3196959.3196962","DOIUrl":"https://doi.org/10.1145/3196959.3196962","url":null,"abstract":"Hypertree decompositions, as well as the more powerful generalized hypertree decompositions (GHDs), and the yet more general fractional hypertree decompositions (FHD) are hypergraph decomposition methods successfully used for answering conjunctive queries and for the solution of constraint satisfaction problems. Every hypergraph H has a width relative to each of these methods: its hypertree width hw(H), its generalized hypertree width ghw(H), and its fractional hypertree width fhw(H), respectively. It is known that hw(H) ≤ k can be checked in polynomial time for fixed k, while checking ghw(H) ≤ k is NP-complete for k >= 3. The complexity of checking fhw(H) ≤ k for a fixed k has been open for over a decade. We settle this open problem by showing that checking fhw(H) ≤ k is NP-complete, even for k=2. The same construction allows us to prove also the NP-completeness of checking ghw(H) ≤ k for k=2. After proving these results, we identify meaningful restrictions, for which checking for bounded ghw or fhw becomes tractable.","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130398196","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":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","authors":"M. Krötzsch, M. Lenzerini, Michael Benedikt","doi":"10.1145/3196959","DOIUrl":"https://doi.org/10.1145/3196959","url":null,"abstract":"","PeriodicalId":344370,"journal":{"name":"Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems","volume":"2 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":"121720803","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}