Efficient Exploration of Interesting Aggregates in RDF Graphs
Yanlei Diao, Paweł Guzewicz, Ioana Manolescu, Mirjana Mazuran
备注：
Accepted for publication in proceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China
As large Open Data are increasingly shared as RDF graphs today, there is a growing demand to help users discover the most interesting facets of a graph, which are often hard to grasp without automatic tools. We consider the problem of automatically identifying the k most interesting aggregate queries that can be evaluated on an RDF graph, given an integer k and a user-specified interestingness function. Our problem departs from analytics in relational data warehouses in that (i) in an RDF graph we are not given but we must identify the facts, dimensions, and measures of candidate aggregates; (ii) the classical approach to efficiently evaluating multiple aggregates breaks in the face of multi-valued dimensions in RDF data. In this work, we propose an extensible end-to-end framework that enables the identification and evaluation of interesting aggregates based on a new RDF-compatible one-pass algorithm for efficiently evaluating a lattice of aggregates and a novel early-stop technique (with probabilistic guarantees) that can prune uninteresting aggregates. Experiments using both real and synthetic graphs demonstrate the ability of our framework to find interesting aggregates in a large search space, the efficiency of our algorithms (with up to 2.9x speedup over a similar pipeline based on existing algorithms), and scalability as the data size and complexity grow.
rdf:SynopsViz - A Framework for Hierarchical Linked Data Visual Exploration and Analysis
数据可视化的目的是为信息感知和操作提供直观的方式，特别是对于非专家用户。Web of Data已经实现了大量数据集的可用性。然而，可用信息的数量和异质性使得人类难以手动探索和分析大型数据集。在本文中，我们提出了rdf：SynopsViz，分层图表和可视化探索的链接开放数据（LOD）的工具。分层LOD探索基于基于一个或多个属性的值创建多个级别的分层相关资源组。所采用的层次模型提供了有效的信息抽象和概括。此外，它还允许高效的动态统计计算，使用层次结构级别上的聚合。
In this paper, we study the following problem: given a knowledge graph (KG) and a set of input vertices (representing concepts or entities) and edge labels, we aim to find the smallest connected subgraphs containing all of the inputs. This problem plays a key role in KG-based search engines and natural language question answering systems, and it is a natural extension of the Steiner tree problem, which is known to be NP-hard. We present RECON, a system for finding approximate answers. RECON aims at achieving high accuracy with instantaneous response (i.e., sub-second/millisecond delay) over KG…
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QCRI, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Toronto（多伦多大学）
Data is generated at an unprecedented rate surpassing our ability to analyze them. The database community has pioneered many novel techniques for Approximate Query Processing (AQP) that could give approximate results in a fraction of time needed for computing exact results. In this work, we explore the usage of deep learning (DL) for answering aggregate queries specifically for interactive applications such as data exploration and visualization. We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be…
