Proceedings of the 21st ACM international conference on Information and knowledge management最新文献_第9页

Incorporating occupancy into frequent pattern mining for high quality pattern recommendation 将占用率纳入频繁的模式挖掘，以获得高质量的模式推荐

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396775

Linpeng Tang, Lei Zhang, Ping Luo, Min Wang

{"title":"Incorporating occupancy into frequent pattern mining for high quality pattern recommendation","authors":"Linpeng Tang, Lei Zhang, Ping Luo, Min Wang","doi":"10.1145/2396761.2396775","DOIUrl":"https://doi.org/10.1145/2396761.2396775","url":null,"abstract":"Mining interesting patterns from transaction databases has attracted a lot of research interest for more than a decade. Most of those studies use frequency, the number of times a pattern appears in a transaction database, as the key measure for pattern interestingness. In this paper, we introduce a new measure of pattern interestingness, occupancy. The measure of occupancy is motivated by some real-world pattern recommendation applications which require that any interesting pattern X should occupy a large portion of the transactions it appears in. Namely, for any supporting transaction t of pattern X, the number of items in X should be close to the total number of items in t. In these pattern recommendation applications, patterns with higher occupancy may lead to higher recall while patterns with higher frequency lead to higher precision. With the definition of occupancy we call a pattern dominant if its occupancy is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both frequent and dominant. Additionally, we also formulate the problem of mining top-k qualified patterns: finding the qualified patterns with the top-k values of any function (e.g. weighted sum of both occupancy and support). The challenge to these tasks is that the monotone or anti-monotone property does not hold on occupancy. In other words, the value of occupancy does not increase or decrease monotonically when we add more items to a given itemset. Thus, we propose an algorithm called DOFIA (DOminant and Frequent Itemset mining Algorithm), which explores the upper bound properties on occupancy to reduce the search process. The tradeoff between bound tightness and computational complexity is also systematically addressed. Finally, we show the effectiveness of DOFIA in a real-world application on print-area recommendation for Web pages, and also demonstrate the efficiency of DOFIA on several large synthetic data sets.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122307766","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}

引用次数: 43

Learning to discover complex mappings from web forms to ontologies 学习发现从web表单到本体的复杂映射

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398427

Yuan An, Xiaohua Hu, I. Song

{"title":"Learning to discover complex mappings from web forms to ontologies","authors":"Yuan An, Xiaohua Hu, I. Song","doi":"10.1145/2396761.2398427","DOIUrl":"https://doi.org/10.1145/2396761.2398427","url":null,"abstract":"In order to realize the Semantic Web, various structures on the Web including Web forms need to be annotated with and mapped to domain ontologies. We present a machine learning-based automatic approach for discovering complex mappings from Web forms to ontologies. A complex mapping associates a set of semantically related elements on a form to a set of semantically related elements in an ontology. Existing schema mapping solutions mainly rely on integrity constraints to infer complex schema mappings. However, it is difficult to extract rich integrity constraints from forms. We show how machine learning techniques can be used to automatically discover complex mappings between Web forms and ontologies. The challenge is how to capture and learn the complicated knowledge encoded in existing complex mappings. We develop an initial solution that takes a naive Bayesian approach. We evaluated the performance of the solution on various domains. Our experimental results show that the solution returns the expected mappings as the top-1 results usually among several hundreds candidate mappings for more than 80% of the test cases. Furthermore, the expected mappings are always returned as the top-k results with k<4. The experiments have demonstrated that the approach is effective and has the potential to save significant human efforts.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122313047","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}

引用次数: 10

A new tool for multi-level partitioning in teradata 在teradata中用于多级分区的新工具

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398604

Young-Kyoon Suh, A. Ghazal, A. Crolotte, Pekka Kostamaa

{"title":"A new tool for multi-level partitioning in teradata","authors":"Young-Kyoon Suh, A. Ghazal, A. Crolotte, Pekka Kostamaa","doi":"10.1145/2396761.2398604","DOIUrl":"https://doi.org/10.1145/2396761.2398604","url":null,"abstract":"This paper introduces a new tool that recommends an optimized partitioning solution called Multi-Level Partitioned Primary Index (MLPPI) for a fact table based on the queries in the workload. The tool implements a new technique using a greedy algorithm for search space enumeration. The space is driven by predicates in the queries. This technique fits very well the Teradata MLPPI scheme, as it is based on a general framework using general expressions, ranges and case expressions for partition definitions. The cost model implemented in the tool is based on the Teradata optimizer, and it is used to prune the search space for reaching a final solution. The tool resides completely on the client, and interfaces the database through APIs as opposed to previous work that requires optimizer code extension. The APIs are used to simplify the workload queries, and to capture fact table predicates and costs necessary to make the recommendation. The predicate-driven method implemented by the tool is general, and it can be applied to any clustering or partitioning scheme based on simple field expressions or complex SQL predicates. Experimental results given a particular workload will show that the recommendation from the tool outperforms a human expert. The experiments also show that the solution is scalable both with the workload complexity and the size of the fact table.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126055669","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}

引用次数: 5

A decentralized recommender system for effective web credibility assessment 一个分散的推荐系统，用于有效的网络可信度评估

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396851

Thanasis G. Papaioannou, Jean-Eudes Ranvier, Alexandra Olteanu, K. Aberer

引用次数: 21

G-SPARQL: a hybrid engine for querying large attributed graphs G-SPARQL:用于查询大型属性图的混合引擎

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396806

S. Sakr, S. Elnikety, Yuxiong He

{"title":"G-SPARQL: a hybrid engine for querying large attributed graphs","authors":"S. Sakr, S. Elnikety, Yuxiong He","doi":"10.1145/2396761.2396806","DOIUrl":"https://doi.org/10.1145/2396761.2396806","url":null,"abstract":"We propose a SPARQL-like language, G-SPARQL, for querying attributed graphs. The language expresses types of queries which of large interest for applications which model their data as large graphs such as: pattern matching, reachability and shortest path queries. Each query can combine both of structural predicates and value-based predicates (on the attributes of the graph nodes and edges). We describe an algebraic compilation mechanism for our proposed query language which is extended from the relational algebra and based on the basic construct of building SPARQL queries, the Triple Pattern. We describe a hybrid Memory/Disk representation of large attributed graphs where only the topology of the graph is maintained in memory while the data of the graph is stored in a relational database. The execution engine of our proposed query language splits parts of the query plan to be pushed inside the relational database while the execution of other parts of the query plan are processed using memory-based algorithms, as necessary. Experimental results on real datasets demonstrate the efficiency and the scalability of our approach and show that our approach outperforms native graph databases by several factors.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368521","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}

引用次数: 67

Towards measuring the visualness of a concept 测量一个概念的可视性

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398655

Jin-Woo Jeong, Xin-Jing Wang, Dong-Ho Lee

引用次数: 12

On using category experts for improving the performance and accuracy in recommender systems 利用品类专家提高推荐系统的性能和准确性

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398639

Won-Seok Hwang, Ho-Jong Lee, Sang-Wook Kim, Minsoo Lee

引用次数: 16

Fast multi-task learning for query spelling correction 快速多任务学习查询拼写纠正

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396800

Xuan Sun, Anshumali Shrivastava, Ping Li

引用次数: 7

TALMUD: transfer learning for multiple domains TALMUD:多领域的迁移学习

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396817

Orly Moreno, Bracha Shapira, L. Rokach, Guy Shani

引用次数: 93

Automated feature weighting in naive bayes for high-dimensional data classification 用于高维数据分类的朴素贝叶斯特征自动加权

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398426

Lifei Chen, Shengrui Wang

引用次数: 29