Proceedings of the 2017 ACM on Conference on Information and Knowledge Management最新文献_第10页

HyPerInsight: Data Exploration Deep Inside HyPer HyPerInsight: HyPerInsight内部的数据探索

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133167

N. Hubig, Linnea Passing, Maximilian E. Schüle, Dimitri Vorona, A. Kemper, Thomas Neumann

{"title":"HyPerInsight: Data Exploration Deep Inside HyPer","authors":"N. Hubig, Linnea Passing, Maximilian E. Schüle, Dimitri Vorona, A. Kemper, Thomas Neumann","doi":"10.1145/3132847.3133167","DOIUrl":"https://doi.org/10.1145/3132847.3133167","url":null,"abstract":"Nowadays we are drowning in data of various varieties. For all these mixed types and categories of data there exist even more different analysis approaches, often done in single hand-written solutions. We propose to extend HyPer, a main memory database system to a uniform data agent platform following the one system fits all approach for solving a wide variety of data analysis problems. We achieve this by applying a flexible operator concept to a set of various important data exploration algorithms. With that, HyPer solves analytical questions using clustering, classification, association rule mining and graph mining besides standard HTAP (Hybrid Transaction and Analytical Processing) workloads on the same database state. It enables to approach the full variety and volume of HTAP extended for data exploration (HTAPx), and only needs knowledge of already introduced SQL extensions that are automatically optimized by the database's standard optimizer. In this demo we will focus on the benefits and flexibility we create by using the SQL extensions for several well-known mining workloads. In our interactive webinterface for this project named HyPerInsight we demonstrate how HyPer outperforms the best open source competitor Apache Spark in common use cases in social media, geo-data, recommender systems and several other.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72678469","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: 13

Boolean Matrix Decomposition by Formal Concept Sampling 布尔矩阵的形式概念抽样分解

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133054

P. Osicka, Martin Trnecka

引用次数: 2

An Enhanced Topic Modeling Approach to Multiple Stance Identification 多姿态识别的增强主题建模方法

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133145

Junjie Lin, W. Mao, Yuhao Zhang

{"title":"An Enhanced Topic Modeling Approach to Multiple Stance Identification","authors":"Junjie Lin, W. Mao, Yuhao Zhang","doi":"10.1145/3132847.3133145","DOIUrl":"https://doi.org/10.1145/3132847.3133145","url":null,"abstract":"People often publish online texts to express their stances, which reflect the essential viewpoints they stand. Stance identification has been an important research topic in text analysis and facilitates many applications in business, public security and government decision making. Previous work on stance identification solely focuses on classifying the supportive or unsupportive attitude towards a certain topic/entity. The other important type of stance identification, multiple stance identification, was largely ignored in previous research. In contrast, multiple stance identification focuses on identifying different standpoints of multiple parties involved in online texts. In this paper, we address the problem of recognizing distinct standpoints implied in textual data. As people are inclined to discuss the topics favorable to their standpoints, topics thus can provide distinguishable information of different standpoints. We propose a topic-based method for standpoint identification. To acquire more distinguishable topics, we further enhance topic model by adding constraints on document-topic distributions. We finally conduct experimental studies on two real datasets to verify the effectiveness of our approach to multiple stance identification.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"46 29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78785308","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

Understanding Database Performance Inefficiencies in Real-world Web Applications 了解实际Web应用程序中数据库性能的低效率

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132954

Cong Yan, Alvin Cheung, Junwen Yang, Shan Lu

引用次数: 39

Broad Learning based Multi-Source Collaborative Recommendation 基于广泛学习的多源协同推荐

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132976

Junxing Zhu, Jiawei Zhang, Lifang He, Quanyuan Wu, Bin Zhou, Chenwei Zhang, Philip S. Yu

{"title":"Broad Learning based Multi-Source Collaborative Recommendation","authors":"Junxing Zhu, Jiawei Zhang, Lifang He, Quanyuan Wu, Bin Zhou, Chenwei Zhang, Philip S. Yu","doi":"10.1145/3132847.3132976","DOIUrl":"https://doi.org/10.1145/3132847.3132976","url":null,"abstract":"Anchor links connect information entities, such as entities of movies or products, across networks from different sources, and thus information in these networks can be transferred directly via anchor links. Therefore, anchor links have great value to many cross-network applications, such as cross-network social link prediction and cross-network recommendation. In this paper, we focus on studying the recommendation problem that can provide ratings of items or services. To address the problem, we propose a Cross-network Collaborative Matrix Factorization (CCMF) recommendation framework based on broad learning setting, which can effectively integrate multi-source information and alleviate the sparse information problem in each individual network. Based on item anchor links CCMF can fuse item similarity information and item latent information across networks from different sources. And different from most of the traditional works, CCMF can make multi-source recommendation tasks collaborate together via the information transfer based on the broad learning setting. During the transfer process, a novel cross-network similarity transfer method is applied to keep the consistency of item similarities between two different networks, and a domain adaptation matrix is used to overcome the domain difference problem. We conduct experiments to compare the proposed CCMF method with both classic and state-of-the-art recommendation techniques. The experimental results illustrate that CCMF outperforms other methods in different experimental circumstances, and has great advantages on dealing with different data sparse problems.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85643842","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: 20

Extracting Records from the Web Using a Signal Processing Approach 使用信号处理方法从Web中提取记录

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132875

R. P. Velloso, C. Dorneles

引用次数: 7

Revealing the Hidden Links in Content Networks: An Application to Event Discovery 揭示内容网络中的隐藏链接:一个事件发现的应用

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133148

Antonia Saravanou, I. Katakis, G. Valkanas, V. Kalogeraki, D. Gunopulos

{"title":"Revealing the Hidden Links in Content Networks: An Application to Event Discovery","authors":"Antonia Saravanou, I. Katakis, G. Valkanas, V. Kalogeraki, D. Gunopulos","doi":"10.1145/3132847.3133148","DOIUrl":"https://doi.org/10.1145/3132847.3133148","url":null,"abstract":"Social networks have become the de facto online resource for people to share, comment on and be informed about events pertinent to their interests and livelihood, ranging from road traffic or an illness to concerts and earthquakes, to economics and politics. This has been the driving force behind research endeavors that analyse such data. In this paper, we focus on how Content Networks can help us identify events effectively. Content Networks incorporate both structural and content-related information of a social network in a unified way, at the same time, bringing together two disparate lines of research: graph-based and content-based event discovery in social media. We model interactions of two types of nodes, users and content, and introduce an algorithm that builds heterogeneous, dynamic graphs, in addition to revealing content links in the network's structure. By linking similar content nodes and tracking connected components over time, we can effectively identify different types of events. Our evaluation on social media streaming data suggests that our approach outperforms state-of-the-art techniques, while showcasing the significance of hidden links to the quality of the results.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84993552","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: 2

Knowledge-based Question Answering by Jointly Generating, Copying and Paraphrasing 联合生成、复制和释义的知识问答

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133064

Shuguang Zhu, Xiang Cheng, Sen Su, S. Lang

{"title":"Knowledge-based Question Answering by Jointly Generating, Copying and Paraphrasing","authors":"Shuguang Zhu, Xiang Cheng, Sen Su, S. Lang","doi":"10.1145/3132847.3133064","DOIUrl":"https://doi.org/10.1145/3132847.3133064","url":null,"abstract":"With the development of large-scale knowledge bases, people are building systems which give simple answers to questions based on consolidate facts. In this paper, we focus on simple questions, which ask about only a subject and relation in the knowledge base. Observing that certain parts of a question usually overlap with names of its corresponding subject and relation in the knowledge base, we argue that a question is formed by a mixture of copying and generation. To model that, we propose a sequence-to-sequence (seq2seq) architecture which encodes a candidate subject-relation pair and decodes it into the given question, where the decoding probability is used to select the best candidate. In our decoder, the copying mode points the subject or relation and duplicates its name, while the generating mode summarizes the meaning of the subject-relation pair and produces a word to smooth the question. Realizing that although sometimes a subject or relation is pointed, different names or keywords might be used, we also incorporate a paraphrasing mode to supplement the copying mode using an automatically mined lexicon. Extensive experiments on the largest dataset exhibit our better performance compared with the state-of-the-art methods.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82542172","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: 8

TaCLe: Learning Constraints in Tabular Data 表格数据中的学习约束

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133193

Sergey Paramonov, Samuel Kolb, Tias Guns, L. D. Raedt

引用次数: 8

When Deep Learning Meets Transfer Learning 当深度学习遇到迁移学习

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3137175

Qiang Yang

引用次数: 8