EDDY 2018 Abstracts

Area 1 - Software Agents and Internet Computing
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Paper Nr: 2
Title:

Design of a Portable Programming Abstraction for Data Transformations
Authors:

Johannes Luong, Dirk Habich and Wolfgang Lehner
Abstract: Novel data intensive applications and the diversification of data processing platforms have changed data management significantly over the last decade. In this changed environment, the expressiveness of the traditional relational algebra is often insufficient and data management systems have started to provide more powerful special purpose programming languages. However, these languages create a tight coupling between applications and specific systems that can hinder further development on both sides of the equation. The goal of this article is to start a discussion on the future of platform independent programming models for data processing that re-establish the separation of application logic and implementation details that used to be a cornerstone of data management systems. As a guide for that discussion, we introduce several recent related works on that topic and also outline our own contribution, the Analytical Calculus.
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Paper Nr: 4
Title:

Self-adaptive Synchronous Localization and Mapping using Runtime Feature Models
Authors:

Christopher Werner, Sebastian Werner, René Schöne, Sebastian Götz and Uwe Aßmann
Abstract: Mobile autonomous robotic systems need to operate in unknown areas. For this, a plethora of simultaneous localization and mapping (SLAM) approaches has been proposed over the last decades. Although many of these existing approaches have been successfully applied even in real-world productive scenarios, they are typically designed for specific contexts (e.g., in-vs. outdoor, crowded vs. free areas, etc.). Thus, for different contexts, different SLAM algorithms should be used. In this paper, we propose a feature-based classification of SLAM algorithms and a reconfiguration approach to switch between existing SLAM implementations at runtime. By this, mobile robots are enabled to always use the most efficient implementation for their current contexts.
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Area 1 - Software Agents and Internet Computing

Full Papers
Paper Nr: 1
Title:

Cracking KD-Tree: The First Multidimensional Adaptive Indexing (Position Paper)
Authors:

Pedro Holanda, Matheus Nerone, Eduardo C. de Almeida and Stefan Manegold
Abstract: Workload-aware physical data access structures are crucial to achieve short response time with (exploratory) data analysis tasks as commonly required for Big Data and Data Science applications. Recently proposed techniques such as automatic index advisers (for a priori known static workloads) and query-driven adaptive incremental indexing (for a priori unknown dynamic workloads) form the state-of-the-art to build single-dimensional indexes for single-attribute query predicates. However, similar techniques for more demanding multi-attribute query predicates, which are vital for any data analysis task, have not been proposed, yet. In this paper, we present our on-going work on a new set of workload-adaptive indexing techniques that focus on creating multidimensional indexes. We present our proof-of-concept, the Cracking KD-Tree, an adaptive indexing approach that generates a KD-Tree based on multidimensional range query predicates. It works by incrementally creating partial multidimensional indexes as a by-product of query processing. The indexes are produced only on those parts of the data that are accessed, and their creation cost is effectively distributed across a stream of queries. Experimental results show that the Cracking KD-Tree is three times faster than creating a full KD-Tree, one order of magnitude faster than executing full scans and two orders of magnitude faster than using uni-dimensional full or adaptive indexes on multiple columns.
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