INGREX: An Interactive Explanation Framework for Graph Neural Networks

Graph Neural Networks (GNNs) are widely used in many modern applications, necessitating explanations for their decisions. However, the complexity of GNNs makes it difficult to explain predictions. Even though several methods have been proposed lately, they can only provide simple and static explanations, which are difficult for users to understand in many scenarios. Therefore, we introduce INGREX, an interactive explanation framework for GNNs designed to aid users in comprehending model predictions. Our framework is implemented based on multiple explanation algorithms and advanced libraries. We demonstrate our framework in three scenarios covering common demands for GNN explanations to present its effectiveness and helpfulness.Comment: 4 pages, 5 figures, This paper is under review for IEEE ICDE 202

Social navigation

In this chapter we present one of the pioneer approaches in supporting users in navigating the complex information spaces, social navigation support. Social navigation support is inspired by natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details on various approaches in implementing social navigation support in the information space as we also connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of example applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach to various other social information access approaches we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state

Load Distribution of Analytical Query Workloads for Database Cluster Architectures

Enterprises may have multiple database systems spread across the organization for redundancy or for serving different applications. In such systems, query workloads can be distributed across different servers for better performance. A materialized view, or Materialized Query Table (MQT), is an auxiliary table with pre-computed data that can be used to significantly improve the performance of a database query. In this paper, we propose a framework for coordinating execution of OLAP query workloads across a database cluster with shared nothing architecture. Such coordination is complex since we need to consider (1) the time to build the MQTs, (2) the query execution impact of the MQTs, (3) whether the MQTs can fit in the disk space limitation, (4) server computation power, and (5) the effectiveness of the scheduling and placement algorithms in deriving a combination of configurations so that the workload can be completed in the shortest time period. We frame the problem as a combinatorial problem with a solution space that is exponential in the number of queries, MQTs, and servers. We provide a stochastic search heuristic that finds a near-optimal mapping of queries-to-servers and MQTs-to-servers within an arbitrarily bounded time and compare our solution with an exhaustive search and three standard greedy algorithms. Our search implementation produced schedules within 9% of the optimal found through an exhaustive search and produced better solutions than typical greedy algorithms for both TPC-H and synthetic benchmarks under a variety of experiments. For a key trial where disk space is limited, it produced 15 % better results than the next best competitor, corresponding to an absolute wall clock advantage of over 10 hours

Resolving Keyword Abbreviation Heterogeneity Using Gene Matching

Introduction As information continues to proliferate in networks throughout the world, the need to access specific portions of data from disparate sources becomes increasingly more important. We see three types of information sources stored in digital libraries: media (audio and video), textual and HTML documents, and database-stored information. The methods to integrate these different types of information sources vary due to the nature of the information presented, which is usually heterogeneous. Media information is integrated via separate display windows. (e.g. MPEG player or SHOWPS). Textual documents can be integrated by converting to HTML formats or abstracts. Database-stored information can be integrated through heterogeneous database semantic integration techniques. It is within this latter domain that we intend to identify a problem and propose a possible solution. 2 The Problem One problem inherent to integrating information from separate autonomou