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

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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

Throughout our digital lives, we are getting recommendations for about almost everything we do, buy or consume. In that way, the field of recommender systems has been evolving vastly to match the increasing user needs accordingly. News, products, ideas and people are only a few of the things that we can be recommended with daily. However, even with the many years of research, several areas still remain unexplored. The focus of this paper revolves around such an area, namely on how to achieve diversity in single-user and group recommendations. Specifically, we decouple diversity from strictly revolving around items, and consider it as an orthogonal dimension that can be incorporated independently at different times in the recommender's workflow. We consider various definitions of diversity, taking into account either data items or users characteristics, and study how to cope with them, depending on whether we opt at diversity-aware single-user or group recommendations.

在我们的数字生活中，我们所做、购买或消费的几乎所有事情都会得到推荐。通过这种方式，推荐系统领域已经得到了巨大的发展，以匹配不断增长的用户需求。新闻、产品、想法和人只是我们每天可以被推荐的一小部分。然而，即使经过多年的研究，仍有几个领域未被探索。本文的重点是围绕这样一个领域，即如何实现单一用户和群体推荐的多样性。具体来说，我们将多样性与严格围绕项目的分离开来，并将其视为一个正交维度，可以在推荐工作流程的不同时间独立合并。我们考虑多样性的各种定义，考虑数据项或用户特征，并研究如何处理它们，这取决于我们是选择具有多样性意识的单个用户还是群体建议。

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

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.

大量的个人数字痕迹不断地从各种来源产生或在线提供，例如社交媒体，日历，购买历史等。这些个人数据轨迹是碎片化的，并且是高度异构的，因此需要对用户的活动进行集成。先前对个人信息管理的研究主要集中在创建世界(对象及其关系)的静态模型上。我们认为，动态世界观也有助于理解相关个人文档的集合，并提出了基于脚本的部分解决方案——这是人工智能和认知科学中理论上有充分根据的想法。脚本是针对日常活动的刻板的等级计划，主要涉及社会代理之间的交互。我们用他们可能留下的数字痕迹来增强这些线索。通过脚本连接个人数字痕迹，我们可以构建用户数字记忆的情景视图，让用户以综合的方式探索相关的事件和行为。本文使用Eating_Out脚本进行说明，最后报告了在实际用户数据上应用原型实现的案例研究结果。

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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

The search for relevant information can be very frustrating for users who, unintentionally, use inappropriate keywords to express their needs. Expansion techniques aim at transforming the users' queries by adding new terms, called expansion features, that better describe the real users' intent. We propose Structural Query Expansion (SQE), a method that relies on relevant structures found in knowledge bases (KBs) to extract the expansion features as opposed to the use of semantics. In the particular case of this paper, we use Wikipedia because it is probably the largest source of up-to-date information. SQE is capable of achieving more than 150% improvement over non-expanded queries and is able to identify the expansion features in less than 0.2 seconds in the worst-case scenario. SQE is designed as an orthogonal method that can be combined with other expansion techniques, such as pseudo-relevance feedback.

搜索相关信息可能会让用户非常沮丧，因为他们无意中使用了不合适的关键字来表达自己的需求。扩展技术旨在通过添加新的术语(称为扩展特征)来转换用户的查询，这些术语更好地描述了用户的真实意图。我们提出了结构化查询扩展(SQE)，这是一种依赖于知识库(KBs)中发现的相关结构来提取扩展特征的方法，而不是使用语义。在本文的特殊情况下，我们使用维基百科，因为它可能是最新信息的最大来源。SQE能够实现比非扩展查询150%以上的改进，并且能够在最坏的情况下在0.2秒内识别扩展特性。SQE被设计为一种正交方法，可以与伪相关反馈等其他扩展技术相结合。

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

Data and metadata suffer many different kinds of change: values are inserted, deleted or updated; entities appear and disappear; properties are added or re-purposed, etc. Explicitly recognizing, exploring, and evaluating such change can alert to changes in data ingestion procedures, can help assess data quality, and can improve the general understanding of the dataset and its behavior over time. We propose a data model-independent framework to formalize such change. Our change-cube enables exploration and discovery of such changes to reveal dataset behavior over time.

数据和元数据遭受许多不同类型的变化:值被插入、删除或更新;实体出现又消失;添加属性或重新使用属性等。明确地识别、探索和评估这种变化可以提醒数据摄取过程中的变化，可以帮助评估数据质量，并且可以随着时间的推移提高对数据集及其行为的总体理解。我们提出了一个独立于数据模型的框架来形式化这种变化。我们的更改多维数据集支持探索和发现这些更改，以揭示数据集随时间的行为。

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

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.

监控应用程序的快速增长导致了生成的时间序列数据的空前数量。数据分析师通常会探索如此大量的时间序列数据，以寻找有价值的见解。其中一种见解是找到时间序列对，其中值的子序列表现出一定程度的相关性。然而，由于探索性查询最初往往是模糊和不精确的，因此分析人员通常会使用一个查询的结果作为制定新查询的跳板，在新查询中进一步细化相关规范。因此，为分析人员的探索性查询提供快速的初始结果是至关重要的，这样可以加快精化过程。当在由大量长时间序列组成的大型搜索空间中探索相关性时，这个目标是具有挑战性的。在这项工作中，我们提出的搜索算法恰恰解决了这一挑战。我们工作的主要思想是设计基于优先级的搜索算法，有效地导航相当大的空间，以快速找到探索性查询的初始结果。实验结果表明，我们的算法优于现有的算法，并且在探索大型时间序列数据时实现了高度的交互性。

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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

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.

相似性搜索是探索性数据分析任务的核心。距离度量通常用于表示为特征向量的数据对象之间的相似性。然而，当数据的维数增加，特征数量很大时，传统的距离度量无法区分最近和最远的数据点。局部距离函数已被提出作为传统距离度量的替代方法。这些函数只考虑接近查询的维度来计算距离/相似度。此外，为了支持对高维数据的交互式探索，需要对特别查询提供索引支持。在这项工作中，我们开始研究位切片索引是否可以用于探索性分析，如相似性搜索和高维大数据的数据聚类。我们还提出了一种新的动态量化，称为查询相关等深度量化(QED)，并证明了它在表征高维相似性方面的有效性。当应用QED时，我们观察到kNN分类精度比传统距离函数有所提高。Acm参考格式:georghi Guzun and Guadalupe canhuate . 2017。支持高维数据分析的动态量化。《Proceedings of Ex-ploreDB’17》，2017年5月14-19日，美国芝加哥，IL, USA, 6页。https://doi.org/http: / / dx.doi.org/10.1145/3077331.3077336。

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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

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