Xiaoyu Ge, Panos K. Chrysanthis, Alexandros Labrinidis
{"title":"Preferential Diversity","authors":"Xiaoyu Ge, Panos K. Chrysanthis, Alexandros Labrinidis","doi":"10.1145/2795218.2795224","DOIUrl":"https://doi.org/10.1145/2795218.2795224","url":null,"abstract":"The ever increasing supply of data is bringing a renewed attention to query personalization. Query personalization is a technique that utilizes user preferences with the goal of providing relevant results to the users. Along with preferences, diversity is another important aspect of query personalization especially useful during data exploration. The goal of result diversification is to reduce the amount of redundant information included in the results. Most previous approaches of result diversification focus solely on generating the most diverse results, which do not take user preferences into account. In this paper, we propose a novel framework called Preferential Diversity (PrefDiv) that aims to support both relevancy and diversity of user query results. PrefDiv utilizes user preference models that return ranked results and reduces the redundancy of results in an efficient and flexible way. PrefDiv maintains the balance between relevancy and diversity of the query results by providing users with the ability to control the trade-off between the two. We describe an implementation of PrefDiv on top of the HYPRE preference model, which allows users to specify both qualitative and quantitative preferences and unifies them using the concept of preference intensities. We experimentally evaluate its performance by comparing with state-of-the-art diversification techniques; our results indicate that PrefDiv achieves significantly better balance between diversity and relevance.","PeriodicalId":211132,"journal":{"name":"Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128370458","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":"Method of Complex Event Processing over XML Streams","authors":"Tatsuki Matsuda, Yuki Uchida, Satoru Fujita","doi":"10.1145/2795218.2795220","DOIUrl":"https://doi.org/10.1145/2795218.2795220","url":null,"abstract":"This paper describes a query processing engine for multiple continuous XML data streams with correlated data as a notification mechanism for navigating data exploration. Stream processing, including formal models for stream filtering, union, activation, decomposition, and partition, is formulated in algebraic expressions. In addition, a query language, called QLMXS, over XML streams for complex event processing is described. QLMXS supports all functions of the algebraic expressions in a SQL-like form. QLMXS queries are converted into a visibly pushdown automaton (VPA) that analyzes complex event data from the XML streams. The VPA engine concurrently processes multiple XML data on multiple levels; therefore, it is very important to tune the performance of the engine. Four optimization methods are proposed to improve performance by utilizing VPA and XML features: VPA-state reduction, VPA unification, delayed evaluation, and elimination of unnecessary XML processing. Experimental results demonstrate that VPA unification increases the processing speed of the VPA engine 1.6 times, and the overall processing speed is increased 2.6 times.","PeriodicalId":211132,"journal":{"name":"Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128381011","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}
V. M. Megler, D. Maier, D. Bottomly, Libbey White, S. McWeeney, B. Wilmot
{"title":"Data Like This: Ranked Search of Genomic Data Vision Paper","authors":"V. M. Megler, D. Maier, D. Bottomly, Libbey White, S. McWeeney, B. Wilmot","doi":"10.1145/2795218.2795221","DOIUrl":"https://doi.org/10.1145/2795218.2795221","url":null,"abstract":"High-throughput genetic sequencing produces the ultimate \"big data\": a human genome sequence contains more than 3B base pairs, and more and more characteristics, or annotations, are being recorded at the base-pair level. Locating areas of interest within the genome is a challenge for researchers, limiting their investigations. We describe our vision of adapting \"big data\" ranked search to the problem of searching the genome. Our goal is to make searching for data as easy for scientists as searching the Internet.","PeriodicalId":211132,"journal":{"name":"Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web","volume":"128 21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115954252","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":"Diversifying with Few Regrets, But too Few to Mention","authors":"Zaeem Hussain, Hina A. Khan, M. Sharaf","doi":"10.1145/2795218.2795225","DOIUrl":"https://doi.org/10.1145/2795218.2795225","url":null,"abstract":"Representative data provide users with a concise overview of their potentially large query results. Recently, diversity maximization has been adopted as one technique to generate representative data with high coverage and low redundancy. Orthogonally, regret minimization has emerged as another technique to generate representative data with high utility that satisfy the user's preference. In reality, however, users typically have some pre-specified preferences over some dimensions of the data, while expecting good coverage over the other dimensions. Motivated by that need, in this work we propose a novel scheme called ReDi, which aims to generate representative data that balance the tradeoff between regret minimization and diversity maximization. ReDi is based on a hybrid objective function that combines both regret and diversity. Additionally, it employs several algorithms that are designed to maximize that objective function. We perform extensive experimental evaluation to measure the tradeoff between the effectiveness and efficiency provided by the different ReDi algorithms.","PeriodicalId":211132,"journal":{"name":"Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121237520","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}