Marco Grawunder

Streaming SPARQL extending SPARQL to process data streams

A lot of work has been done in the area of data stream processing. Most of the previous approaches regard only relational or XML based streams but do not cover semantically richer RDF based stream elements. In our work, we extend SPARQL, the W3C recommendation for an RDF query language, to process RDF data streams. To describe the semantics of our enhancement, we extended the logical SPARQL algebra for stream processing on the foundation of a temporal relational algebra based on multi-sets and provide an algorithm to transform SPARQL queries to the new extended algebra. For each logical algebra operator, we define executable physical counterparts. To show the feasibility of our approach, we implemented it within our ODYSSEUS framework in the context of wind power plant monitoring.

Odysseus: a highly customizable framework for creating efficient event stream management systems

Odysseus is a flexible, feature-rich and extensible framework to design event stream management systems and was developed to support research in event stream processing. It provides a systematic approach to define sources and queries, execution and presentation of results. Odysseus offers basic functionality for fast deployment, but due to its modular architecture, users can easily configure and expand them to meet a large set of applications and research questions.

Data Stream Management in the AAL: Universal and Flexible Preprocessing of Continuous Sensor Data

Continuous and potentially infinite sequences of data—so-called data streams—are processed in many applications of Ambient Assisted Living (AAL). The preprocessing of such high frequent data is normally done by fixed code or hard wired hardware. This leads on the one hand to an inflexible and extensive to change processing and on the other hand to very specialized solutions. Like databases data stream management systems (DSMS) offer a universal processing of data, but are designed for highly frequent and potentially infinite data streams. Thus, DSMS are an alternative approach for processing sensor data. Therefore, this paper shows how DSMS can be used in the AAL for an universal and flexible preprocessing of sensor data. For this, DSMS and its features are introduced and we show which advantages over existing solutions a DSMS can offer for future researches in AAL.

Experiences with Sensor-Based Research for Critical, Socio-technical Systems

The complexity of critical systems such as traffic management has dramatically increased over the last decades, since they involve more and more sensors to derive distributed situational awareness. Most existing systems require an a-priori configuration of sensors or need human intervention to adapt to changes. Furthermore, analysis of data quality or query plan reliability is often not possible and management of recorded data is done by hand. Our goal is to support the research, development, evaluation and demonstration of such systems. Within this study we analyze requirements and challenges for the data management of sensor based research environments and present a data stream based architecture which fulfills these requirements.

Prediction functions in bi-temporal datastreams

Modern datastream management system (DSMS) assume sensor measurements to be constant valued until an update is measured. They do not consider continuously changing measurement values, although a lot of real world scenarios exist that need this essential property. For instance, modern cars use sensors, like radar, to periodically detect dynamic objects like other vehicles. The state of these objects (position and bearing) changes continuously, so that it must be predicted between two measurements. Therefore, in our work we develop a new bitemporal stream algebra for processing continuously changing stream data. One temporal dimension covers correct order of stream elements and the other covers continuously changing measurements. Our approach guarantees deterministic query results and correct optimizability. Our implementation shows that prediction functions can be processed very efficiently.

Using odysseus for real-time analysis over high volume geospatial data streams

In this paper, we provide a solution for the ACM DEBS Grand Challenge 2015 (GC 2015) that deals with the analysis of taxi trips in New York based on the data stream management framework Odysseus [1].

Scheduling von Datenströmen auf der Basis von Service Level Agreements

ZusammenfassungSoftware as a Service (SaaS) stellt einen Ansatz zur Bereitstellung standardisierbarer Software über das Internet dar. Auch die Verarbeitungskapazität von Datenstrommanagementsystemen (DSMS) kann auf diese Weise verschiedenen Kunden kostengünstig zugänglich gemacht werden. Der Anbieter garantiert dem Kunden eine gewisse Dienstqualität in Form von Service Level Agreements (SLAs), deren Verletzung i.d.R. finanzielle Konsequenzen hat. Der Dienstanbieter ist also zum einen daran interessiert, möglichst geringe Kosten durch die SLA-Verletzung zu verursachen, zum anderen eine optimale Systemressourcenauslastung (CPU, Hauptspeicher, etc.) zu erzielen. Diese Ressourcen werden in DSMS mit Hilfe von Schedulingverfahren den kontinuierlichen Anfragen zugewiesen. In unserer Arbeit haben wir ein aus dem Datenbankkontext stammendes Schedulingverfahren für Datenströme angepasst, das sowohl die durch Verletzungen von SLAs entstehenden Kosten aus Sicht des Anbieters berücksichtigt als auch eine effiziente Ressourcenverteilung ermöglicht. In Simulationen haben wir gezeigt, dass sich durch ein solches Scheduling die Kosten des Anbieters senken lassen.

GeoStreams: A Survey

Positional data from small and mobile Global Positioning Systems has become ubiquitous and allows for many new applications such as road traffic or vessel monitoring as well as location-based services. To make these applications possible, for which information on location is more important than ever, streaming spatial data needs to be managed, mined, and used intelligently. This article provides an overview of previous work in this evolving research field and discusses different applications as well as common problems and solutions. The conclusion indicates promising directions for future research.Positional data from small and mobile Global Positioning Systems has become ubiquitous and allows for many new applications such as road traffic or vessel monitoring as well as location-based services. To make these applications possible, for which information on location is more important than ever, streaming spatial data needs to be managed, mined, and used intelligently. This article provides an overview of previous work in this evolving research field and discusses different applications as well as common problems and solutions. The conclusion indicates promising directions for future research.

Moving Object Stream Processing With Short-Time Prediction

Spatio-temporal data from Moving Objects is often available as a live data stream and needs to be processed accordingly. The trajectories update while the objects are moving. Even though the data arrives in a streaming manner, significant delays between location updates are possible, resulting in delayed or less accurate results of continuous queries on the trajectories. That can be an important issue when using the queries for real-world decisions, e. g., with Automatic Identification System (AIS) data for maritime navigation. Additionally, short-time predictions can be useful to get early warnings for critical situations. Previous work does not cover this problem for streaming applications, as existing systems are mainly Moving Object Databases, which are not optimized for streaming data. In this work, we describe how spatio-temporal inter and extrapolation can be integrated into Data Stream Management Systems and which challenges have to be solved doing so.

Demo: Dynamic Generation of Adaptive Real-Time Dashboards for Continuous Data Stream Processing

Conventional database management systems are usually not capable to deal with continuous processing of potentially infinite data streams. Therefore, special data stream management systems and frameworks are developed. They use continuous queries which produce also streams as results, so that static visualization is not feasible, since the results are changing constantly. To handle this, we developed a dashboard concept, which we want to propose in this demonstration. A dashboard can be considered as an control or monitoring panel for real time data stream results. The user is free to define and configure individual dashboard parts. Each part is connected to a (user defined) continuous query, whose results are received and visualized in real-time. In this demonstration, we provide different data stream sources, continuous queries and dashboard parts. With them, the user can compose his own individual presentation of his processing results.