Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications

In our understanding, a mind-map is an adaptive engine that basically works incrementally on the fundament of existing transactional streams. Generally, mind-maps consist of symbolic cells that are connected with each other and that become either stronger or weaker depending on the transactional stream. Based on the underlying biologic principle, these symbolic cells and their connections as well may adaptively survive or die, forming different cell agglomerates of arbitrary size. In this work, we intend to prove mind-maps' eligibility following diverse application scenarios, for example being an underlying management system to represent normal and abnormal traffic behaviour in computer networks, supporting the detection of the user behaviour within search engines, or being a hidden communication layer for natural language interaction.Comment: 4 pages; 4 figure

An On-the-fly Provenance Tracking Mechanism for Stream Processing Systems

Applications that operate over streaming data withhigh-volume and real-time processing requirements are becomingincreasingly important. These applications process streamingdata in real-time and deliver instantaneous responses to supportprecise and on-time decisions. In such systems, traceability -the ability to verify and investigate the source of a particularoutput - in real-time is extremely important. This ability allowsraw streaming data to be checked and processing steps to beverified and validated in timely manner. Therefore, it is crucialthat stream systems have a mechanism for dynamically trackingprovenance - the process that produced result data - at executiontime, which we refer to as on-the-fly stream provenance tracking.In this paper, we propose a novel on-the-fly provenance trackingmechanism that enables provenance queries to be performeddynamically without requiring provenance assertions to be storedpersistently. We demonstrate how our provenance mechanismworks by means of an on-the-fly provenance tracking algorithm.The experimental evaluation shows that our provenance solutiondoes not have a significant effect on the normal processing ofstream systems given a 7% overhead. Moreover, our provenancesolution offers low-latency processing (0.3 ms per additionalcomponent) with reasonable memory consumption.<br/

Mercury: using the QuPreSS reference model to evaluate predictive services

Nowadays, lots of service providers offer predictive services that show in advance a condition or occurrence about the future. As a consequence, it becomes necessary for service customers to select the predictive service that best satisfies their needs. The QuPreSS reference model provides a standard solution for the selection of predictive services based on the quality of their predictions. QuPreSS has been designed to be applicable in any predictive domain (e.g., weather forecasting, economics, and medicine). This paper presents Mercury, a tool based on the QuPreSS reference model and customized to the weather forecast domain. Mercury measures weather predictive services' quality, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared to real observations obtained from a trusted source. Mercury is a proof-of-concept of QuPreSS that aims to show that the selection of predictive services can be driven by the quality of their predictions. Throughout the paper, we show how Mercury was built from the QuPreSS reference model and how it can be installed and used.Peer ReviewedPostprint (author's final draft