Reactive Rules for Emergency Management

The goal of the following survey on Event-Condition-Action (ECA) Rules is to come to a common understanding and intuition on this topic within EMILI. Thus it does not give an academic overview on Event-Condition-Action Rules which would be valuable for computer scientists only. Instead the survey tries to introduce Event-Condition-Action Rules and their use for emergency management based on real-life examples from the use-cases identified in Deliverable 3.1. In this way we hope to address both, computer scientists and security experts, by showing how the Event-Condition-Action Rule technology can help to solve security issues in emergency management. The survey incorporates information from other work packages, particularly from Deliverable D3.1 and its Annexes, D4.1, D2.1 and D6.2 wherever possible

StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30

Scalable Model-Based Management of Correlated Dimensional Time Series in ModelarDB+

To monitor critical infrastructure, high quality sensors sampled at a high frequency are increasingly used. However, as they produce huge amounts of data, only simple aggregates are stored. This removes outliers and fluctuations that could indicate problems. As a remedy, we present a model-based approach for managing time series with dimensions that exploits correlation in and among time series. Specifically, we propose compressing groups of correlated time series using an extensible set of model types within a user-defined error bound (possibly zero). We name this new category of model-based compression methods for time series Multi-Model Group Compression (MMGC). We present the first MMGC method GOLEMM and extend model types to compress time series groups. We propose primitives for users to effectively define groups for differently sized data sets, and based on these, an automated grouping method using only the time series dimensions. We propose algorithms for executing simple and multi-dimensional aggregate queries on models. Last, we implement our methods in the Time Series Management System (TSMS) ModelarDB (ModelarDB+). Our evaluation shows that compared to widely used formats, ModelarDB+ provides up to 13.7 times faster ingestion due to high compression, 113 times better compression due to the adaptivity of GOLEMM, 630 times faster aggregates by using models, and close to linear scalability. It is also extensible and supports online query processing.Comment: 12 Pages, 28 Figures, and 1 Tabl

DKA-robo: dynamically updating time-invalid knowledge bases using robots

In this paper we present the DKA-robo framework, where a mobile agent is used to update those statements of a knowledge base that have lost validity in time. Managing the dynamic information of knowledge bases constitutes a key issue in many real-world scenarios, because constantly reevaluating data requires efforts in terms of knowledge acquisition and representation. Our solution to such a problem is to use RDF and SPARQL to represent and manage the time-validity of information, combined with an agent acting as a mobile sensor which updates the outdated statements in the knowledge base, therefore always guaranteeing time-valid results against user queries. This demo shows the implementation of our approach in the working environment of our research lab, where a robot is used to sense temperature, humidity, wifi- signal and number of people on demand, updating the lab knowledge base with time-valid information

A policy language definition for provenance in pervasive computing

Recent advances in computing technology have led to the paradigm of pervasive computing, which provides a means of simplifying daily life by integrating information processing into the everyday physical world. Pervasive computing draws its power from knowing the surroundings and creates an environment which combines computing and communication capabilities. Sensors that provide high-resolution spatial and instant measurement are most commonly used for forecasting, monitoring and real-time environmental modelling. Sensor data generated by a sensor network depends on several influences, such as the configuration and location of the sensors or the processing performed on the raw measurements. Storing sufficient metadata that gives meaning to the recorded observation is important in order to draw accurate conclusions or to enhance the reliability of the result dataset that uses this automatically collected data. This kind of metadata is called provenance data, as the origin of the data and the process by which it arrived from its origin are recorded. Provenance is still an exploratory field in pervasive computing and many open research questions are yet to emerge. The context information and the different characteristics of the pervasive environment call for different approaches to a provenance support system. This work implements a policy language definition that specifies the collecting model for provenance management systems and addresses the challenges that arise with stream data and sensor environments. The structure graph of the proposed model is mapped to the Open Provenance Model in order to facilitating the sharing of provenance data and interoperability with other systems. As provenance security has been recognized as one of the most important components in any provenance system, an access control language has been developed that is tailored to support the special requirements of provenance: fine-grained polices, privacy policies and preferences. Experimental evaluation findings show a reasonable overhead for provenance collecting and a reasonable time for provenance query performance, while a numerical analysis was used to evaluate the storage overhead