Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.)最新文献

On Achieving Diversity in Recommender Systems 论在推荐系统中实现多样性

Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.) Pub Date : 2017-05-14 DOI: 10.1145/3077331.3077341

Marialena Kyriakidi, K. Stefanidis, Y. Ioannidis

引用次数: 12

Integration and Exploration of Connected Personal Digital Traces 互联个人数字痕迹的整合与探索

Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.) Pub Date : 2017-05-14 DOI: 10.1145/3077331.3077337

Varvara Kalokyri, Alexander Borgida, A. Marian, Daniela Vianna

{"title":"Integration and Exploration of Connected Personal Digital Traces","authors":"Varvara Kalokyri, Alexander Borgida, A. Marian, Daniela Vianna","doi":"10.1145/3077331.3077337","DOIUrl":"https://doi.org/10.1145/3077331.3077337","url":null,"abstract":"A large number of personal digital traces is constantly generated or available online from a variety of sources, such as social media, calendars, purchase history, etc. These personal data traces are fragmented and highly heterogeneous, raising the need for an integrated view of the user's activities. Prior research in Personal Information Management focused mostly on creating a static model of the world (objects and their relationships). We argue that a dynamic world view is also helpful for making sense of collections of related personal documents, and propose a partial solution based on scripts -- a theoretically well-founded idea in AI and Cognitive Science. Scripts are stereotypical hierarchical plans for everyday activities, involving interactions between mostly social agents. We augment these with hints of the digital traces that they can leave. By connecting Personal Digital Traces through scripts, we can build an episodic view of users' digital memories, which allow users to explore related events and actions in an integrated way. The paper uses the Eating_Out script for illustration, and ends with a report on the results of a case-study of applying a prototype implementation on real user data.","PeriodicalId":92430,"journal":{"name":"Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.)","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81417420","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: 6

Structural Query Expansion via motifs from Wikipedia 通过维基百科的主题进行结构查询扩展

Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.) Pub Date : 2017-05-14 DOI: 10.1145/3077331.3077342

Joan Guisado-Gámez, Arnau Prat-Pérez, J. Larriba-Pey

引用次数: 3

Enabling Change Exploration: Vision Paper 启用变更探索:远景文件

Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.) Pub Date : 2017-05-14 DOI: 10.1145/3077331.3077340

Tobias Bleifuß, T. Johnson, D. Kalashnikov, Felix Naumann, Vladislav Shkapenyuk, D. Srivastava

引用次数: 1

Interactive Exploration of Correlated Time Series 相关时间序列的交互式探索

Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.) Pub Date : 2017-05-14 DOI: 10.1145/3077331.3077335

Daniel Petrov, Rakan Alseghayer, M. Sharaf, Panos K. Chrysanthis, Alexandros Labrinidis

{"title":"Interactive Exploration of Correlated Time Series","authors":"Daniel Petrov, Rakan Alseghayer, M. Sharaf, Panos K. Chrysanthis, Alexandros Labrinidis","doi":"10.1145/3077331.3077335","DOIUrl":"https://doi.org/10.1145/3077331.3077335","url":null,"abstract":"The rapid growth of monitoring applications has led to unprecedented amounts of generated time series data. Data analysts typically explore such large volumes of time series data looking for valuable insights. One such insight is finding pairs of time series, in which subsequences of values exhibit certain levels of correlation. However, since exploratory queries tend to be initially vague and imprecise, an analyst will typically use the results of one query as a springboard to formulating a new one, in which the correlation specifications are further refined. As such, it is essential to provide analysts with quick initial results to their exploratory queries, which allows for speeding up the refinement process. This goal is challenging when exploring the correlation in a large search space that consists of a big number of long time series. In this work we propose search algorithms that address precisely that challenge. The main idea underlying our work is to design priority-based search algorithms that efficiently navigate the rather large space to quickly find the initial results of an exploratory query. Our experimental results show that our algorithms outperform existing ones and enable high degree of interactivity in exploring large time series data.","PeriodicalId":92430,"journal":{"name":"Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.)","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86192684","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: 7

Supporting Dynamic Quantization for High-Dimensional Data Analytics. 支持高维数据分析的动态量化。

Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.) Pub Date : 2017-05-01 DOI: 10.1145/3077331.3077336

Gheorghi Guzun, Guadalupe Canahuate

{"title":"Supporting Dynamic Quantization for High-Dimensional Data Analytics.","authors":"Gheorghi Guzun, Guadalupe Canahuate","doi":"10.1145/3077331.3077336","DOIUrl":"https://doi.org/10.1145/3077331.3077336","url":null,"abstract":"Similarity searches are at the heart of exploratory data analysis tasks. Distance metrics are typically used to characterize the similarity between data objects represented as feature vectors. However, when the dimensionality of the data increases and the number of features is large, traditional distance metrics fail to distinguish between the closest and furthest data points. Localized distance functions have been proposed as an alternative to traditional distance metrics. These functions only consider dimensions close to query to compute the distance/similarity. Furthermore, in order to enable interactive explorations of high-dimensional data, indexing support for ad-hoc queries is needed. In this work we set up to investigate whether bit-sliced indices can be used for exploratory analytics such as similarity searches and data clustering for high-dimensional big-data. We also propose a novel dynamic quantization called Query dependent Equi-Depth (QED) quantization and show its effectiveness on characterizing high-dimensional similarity. When applying QED we observe improvements in kNN classification accuracy over traditional distance functions.Acm reference format: Gheorghi Guzun and Guadalupe Canahuate. 2017. Supporting Dynamic Quantization for High-Dimensional Data Analytics. In Proceedings of Ex-ploreDB'17, Chicago, IL, USA, May 14-19, 2017, 6 pages. https://doi.org/http://dx.doi.org/10.1145/3077331.3077336.","PeriodicalId":92430,"journal":{"name":"Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.)","volume":"2017 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3077331.3077336","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36096037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Proceedings of the ExploreDB'17 17年探索性数据库论文集

Proceedings of the ExploreDB'17. International Workshop on Exploratory Search in Databases and the Web (4th : 2017 : Chicago, Ill.) Pub Date : 2017-01-01 DOI: 10.1145/3077331

引用次数: 0