Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data最新文献

{"title":"MaskIt: privately releasing user context streams for personalized mobile applications","authors":"M. Götz, Suman Nath, J. Gehrke","doi":"10.1145/2213836.2213870","DOIUrl":"https://doi.org/10.1145/2213836.2213870","url":null,"abstract":"The rise of smartphones equipped with various sensors has enabled personalization of various applications based on user contexts extracted from sensor readings. At the same time it has raised serious concerns about the privacy of user contexts. In this paper, we present MASKIT, a technique to filter a user context stream that provably preserves privacy. The filtered context stream can be released to applications or be used to answer their queries. Privacy is defined with respect to a set of sensitive contexts specified by the user. MASKIT limits what adversaries can learn from the filtered stream about the user being in a sensitive context - even if the adversaries are powerful and have knowledge about the filtering system and temporal correlations in the context stream. At the heart of MASKIT is a privacy check deciding whether to release or suppress the current user context. We present two novel privacy checks and explain how to choose the one with the higher utility for a user. Our experiments on real smartphone context traces of 91 users demonstrate the high utility of MASKIT.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114564787","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":"Top-k bounded diversification","authors":"P. Fraternali, D. Martinenghi, M. Tagliasacchi","doi":"10.1145/2213836.2213884","DOIUrl":"https://doi.org/10.1145/2213836.2213884","url":null,"abstract":"This paper investigates diversity queries over objects embedded in a low-dimensional vector space. An interesting case is provided by spatial Web objects, which are produced in great quantity by location-based services that let users attach content to places, and arise also in trip planning, news analysis, and real estate scenarios. The targeted queries aim at retrieving the best set of objects relevant to given user criteria and well distributed over a region of interest. Such queries are a particular case of diversified top-k queries, for which existing methods are too costly, as they evaluate diversity by accessing and scanning all relevant objects, even if only a small subset is needed. We therefore introduce Space Partitioning and Probing (SPP), an algorithm that minimizes the number of accessed objects while finding exactly the same result as MMR, the most popular diversification algorithm. SPP belongs to a family of algorithms that rely only on score-based and distance-based access methods, which are available in most geo-referenced Web data sources, and do not require retrieving all the relevant objects. Experiments show that SPP significantly reduces the number of accessed objects while incurring a very low computational overhead.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133599420","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":"SkewTune: mitigating skew in mapreduce applications","authors":"YongChul Kwon, M. Balazinska, Bill Howe, J. Rolia","doi":"10.1145/2213836.2213840","DOIUrl":"https://doi.org/10.1145/2213836.2213840","url":null,"abstract":"We present an automatic skew mitigation approach for user-defined MapReduce programs and present SkewTune, a system that implements this approach as a drop-in replacement for an existing MapReduce implementation. There are three key challenges: (a) require no extra input from the user yet work for all MapReduce applications, (b) be completely transparent, and (c) impose minimal overhead if there is no skew. The SkewTune approach addresses these challenges and works as follows: When a node in the cluster becomes idle, SkewTune identifies the task with the greatest expected remaining processing time. The unprocessed input data of this straggling task is then proactively repartitioned in a way that fully utilizes the nodes in the cluster and preserves the ordering of the input data so that the original output can be reconstructed by concatenation. We implement SkewTune as an extension to Hadoop and evaluate its effectiveness using several real applications. The results show that SkewTune can significantly reduce job runtime in the presence of skew and adds little to no overhead in the absence of skew.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124835877","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":"Holistic optimization by prefetching query results","authors":"Karthik Ramachandra, Sundararajarao Sudarshan","doi":"10.1145/2213836.2213852","DOIUrl":"https://doi.org/10.1145/2213836.2213852","url":null,"abstract":"In this paper we address the problem of optimizing performance of database/web-service backed applications by means of automatically prefetching query results. Prefetching has been performed in earlier work based on predicting query access patterns; however such prediction is often of limited value, and can perform unnecessary prefetches. There has been some earlier work on program analysis and rewriting to automatically insert prefetch requests; however, such work has been restricted to rewriting of single procedures. In many cases, the query is in a procedure which does not offer much scope for prefetching within the procedure; in contrast, our approach can perform prefetching in a calling procedure, even when the actual query is in a called procedure, thereby greatly improving the benefits due to prefetching. Our approach does not perform any intrusive changes to the source code, and places prefetch instructions at the earliest possible points while avoiding wasteful prefetches. We have incorporated our techniques into a tool for holistic optimization called DBridge, to prefetch query results in Java programs that use JDBC. Our tool can be easily extended to handle Hibernate API calls as well as Web service requests. Our experiments on several real world applications demonstrate the applicability and significant performance gains due to our techniques.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127193265","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":"InfoGather: entity augmentation and attribute discovery by holistic matching with web tables","authors":"M. Yakout, Kris Ganjam, K. Chakrabarti, S. Chaudhuri","doi":"10.1145/2213836.2213848","DOIUrl":"https://doi.org/10.1145/2213836.2213848","url":null,"abstract":"The Web contains a vast corpus of HTML tables, specifically entity attribute tables. We present three core operations, namely entity augmentation by attribute name, entity augmentation by example and attribute discovery, that are useful for \"information gathering\" tasks (e.g., researching for products or stocks). We propose to use web table corpus to perform them automatically. We require the operations to have high precision and coverage, have fast (ideally interactive) response times and be applicable to any arbitrary domain of entities. The naive approach that attempts to directly match the user input with the web tables suffers from poor precision and coverage. Our key insight is that we can achieve much higher precision and coverage by considering indirectly matching tables in addition to the directly matching ones. The challenge is to be robust to spuriously matched tables: we address it by developing a holistic matching framework based on topic sensitive pagerank and an augmentation framework that aggregates predictions from multiple matched tables. We propose a novel architecture that leverages preprocessing in MapReduce to achieve extremely fast response times at query time. Our experiments on real-life datasets and 573M web tables show that our approach has (i) significantly higher precision and coverage and (ii) four orders of magnitude faster response times compared with the state-of-the-art approach.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129164161","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":"Online windowed subsequence matching over probabilistic sequences","authors":"Zheng Li, Tingjian Ge","doi":"10.1145/2213836.2213868","DOIUrl":"https://doi.org/10.1145/2213836.2213868","url":null,"abstract":"Windowed subsequence matching over deterministic strings has been studied in previous work in the contexts of knowledge discovery, data mining, and molecular biology. However, we observe that in these applications, as well as in data stream monitoring, complex event processing, and time series data processing in which streams can be mapped to strings, the strings are often noisy and probabilistic. We study this problem in the online setting where efficiency is paramount. We first formulate the query semantics, and propose an exact algorithm. Then we propose a randomized approximation algorithm that is faster and, in the mean time, provably accurate. Moreover, we devise a filtering algorithm to further enhance the efficiency with an optimization technique that is adaptive to sequence stream contents. Finally, we propose algorithms for patterns with negations. In order to verify the algorithms, we conduct a systematic empirical study using three real datasets and some synthetic datasets.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121416901","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 optimization in microsoft SQL server PDW","authors":"S. Shankar, Rimma V. Nehme, J. Aguilar-Saborit, Andrew Chung, Mostafa Elhemali, A. Halverson, Eric Robinson, Mahadevan Subramanian, D. DeWitt, C. Galindo-Legaria","doi":"10.1145/2213836.2213953","DOIUrl":"https://doi.org/10.1145/2213836.2213953","url":null,"abstract":"In recent years, Massively Parallel Processors have increasingly been used to manage and query vast amounts of data. Dramatic performance improvements are achieved through distributed execution of queries across many nodes. Query optimization for such system is a challenging and important problem. In this paper we describe the Query Optimizer inside the SQL Server Parallel Data Warehouse product (PDW QO). We leverage existing QO technology in Microsoft SQL Server to implement a cost-based optimizer for distributed query execution. By properly abstracting metadata we can readily reuse existing logic for query simplification, space exploration and cardinality estimation. Unlike earlier approaches that simply parallelize the best serial plan, our optimizer considers a rich space of execution alternatives, and picks one based on a cost-model for the distributed execution environment. The result is a high-quality, effective query optimizer for distributed query processing in an MPP.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115613648","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":"Designing a scalable crowdsourcing platform","authors":"C. V. Pelt, A. Sorokin","doi":"10.1145/2213836.2213951","DOIUrl":"https://doi.org/10.1145/2213836.2213951","url":null,"abstract":"Computers are extremely efficient at crawling, storing and processing huge volumes of structured data. They are great at exploiting link structures to generate valuable knowledge. Yet there are plenty of data processing tasks that are difficult today. Labeling sentiment, moderating images, and mining structured content from the web are still too hard for computers. Automated techniques can get us a long way in some of those, but human inteligence is required when an accurate decision is ultimately important. In many cases that decision is easy for people and can be made quickly - in a few seconds to few minutes. By creating millions of simple online tasks we create a distributed computing machine. By shipping the tasks to millions of contributers around the globe, we make this human computer available 24/7 to make important decisions about your data. In this talk, I will describe our approach to designing CrowdFlower - a scalable crowdsourcing platform - as it evolved over the last 4 years. We think about crowdsourcing in terms of Quality, Cost and Speed. They are the ultimate design objectives of a human computer. Unfortunately, we can't have all 3. A general price-constrained task requiring 99.9% accuracy and 10 minute turnaround is not possible today. I will discuss design decisions behind CrowdFlower that allow us to pursue any two of these objectives. I will briefly present examples of common crowdsourced tasks and tools built into the platform to make the design of complex tasks easy, tools such as CrowdFlower Markup Language(CML). Quality control is the single most important challenge in Crowdsourcing. To enable an unidentified crowd of people to produce meaningful work, we must be certain that we can filter out bad contributors and produce high quality output. Initially we only used consensus. As the diversity and size of our crowd grew, so did the number of people attempting fraud. CrowdFlower developed \"Gold standard\" to block attempts of fraud. The use of gold allowed us to train contributors for the details of specific domains. By defining expected responses for a subset of the work and providing explanations of why a given response was expected, we are able distribute tasks to an ever-expanding anonymous workforce without sacrificing quality.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115438942","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":"High-performance complex event processing over XML streams","authors":"Barzan Mozafari, Kai Zeng, C. Zaniolo","doi":"10.1145/2213836.2213866","DOIUrl":"https://doi.org/10.1145/2213836.2213866","url":null,"abstract":"Much research attention has been given to delivering high-performance systems that are capable of complex event processing (CEP) in a wide range of applications. However, many current CEP systems focus on processing efficiently data having a simple structure, and are otherwise limited in their ability to support efficiently complex continuous queries on structured or semi-structured information. However, XML streams represent a very popular form of data exchange, comprising large portions of social network and RSS feeds, financial records, configuration files, and similar applications requiring advanced CEP queries. In this paper, we present the XSeq language and system that support CEP on XML streams, via an extension of XPath that is both powerful and amenable to an efficient implementation. Specifically, the XSeq language extends XPath with natural operators to express sequential and Kleene-* patterns over XML streams, while remaining highly amenable to efficient implementation. XSeq is designed to take full advantage of recent advances in the field of automata on Visibly Pushdown Automata (VPA), where higher expressive power can be achieved without compromising efficiency (whereas the amenability to efficient implementation was not demonstrated in XPath extensions previously proposed). We illustrate XSeq's power for CEP applications through examples from different domains, and provide formal results on its expressiveness and complexity. Finally, we present several optimization techniques for XSeq queries. Our extensive experiments indicate that XSeq brings outstanding performance to CEP applications: two orders of magnitude improvement are obtained over the same queries executed in general-purpose XML engines.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128483010","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":"Optimizing index for taxonomy keyword search","authors":"Bolin Ding, Haixun Wang, R. Jin, Jiawei Han, Zhongyuan Wang","doi":"10.1145/2213836.2213892","DOIUrl":"https://doi.org/10.1145/2213836.2213892","url":null,"abstract":"Query substitution is an important problem in information retrieval. Much work focuses on how to find substitutes for any given query. In this paper, we study how to efficiently process a keyword query whose substitutes are defined by a given taxonomy. This problem is challenging because each term in a query can have a large number of substitutes, and the original query can be rewritten into any of their combinations. We propose to build an additional index (besides inverted index) to efficiently process queries. For a query workload, we formulate an optimization problem which chooses the additional index structure, aiming at minimizing the query evaluation cost, under given index space constraints. We show the NP-hardness of the problem, and propose a pseudo-polynomial time algorithm using dynamic programming, as well as an 1 over 4(1-1/e)-approximation algorithm to solve the problem. Experimental results show that, with only 10% additional index space, our approach can greatly reduce the query evaluation cost.","PeriodicalId":212616,"journal":{"name":"Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125360815","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}