Alexandra Moraru

Exposing real world information for the web of things

In this paper, we propose SemSense architecture for collecting real world data from a physical system of sensors and publishing it on the Web, thus contributing to the Web of Things. SemSense comprises of four components: (1) the data collection component, (2) the storage component (3) the semantic enrichment component and (4) the publishing component, which are described and implemented for an existing deployment of a sensor network. Through these components, the real world data is collected from the physical devices, processed, equipped with semantic information and published on the Web. The paper addresses challenges of efficiently collecting data and meta-data from sensors and publishing it following the linked data principles.

A hybrid approach to QoS-aware Web Service classification and recommendation

The proliferation of Web Services (WSs) has given significant importance to the Quality of Service (QoS) in the discovery process. This paper introduces a hybrid system which combines semantic technology and logic based reasoning (i.e. OpenCyc) with numerical calculus to classify, evaluate and then recommend QoS aware WSs. The system is able to answer high level composed functional and/or nonfunctional queries related to a real-life dataset containing 2500 WS, therefore enabling classification and recommendation of WSs.

Mashups for the Web of Things

The Web of Things (WoT) together with mashup-like applications is gaining popularity with the development of the Internet towards a network of interconnected objects, ranging from cars and transportation cargos to electrical appliances. In this chapter we provide a brief architectural overview of technologies which can be used in WoT mashups with emphasis on artificial intelligence technologies such as conceptualization and stream processing. We also look at data sources and existing WoT mashups. In the last part of the chapter we discuss the architecture and implementation of Videk, a prototype mashup for environmental intelligence.

A framework for semantic enrichment of sensor data

The increased interest in sensing the environment in which we live has led to the deployment of thousands of sensors which can measure and report its status. In order to raise the impact that sensor networks can have, improving the usability and accessibility of the measurements they provide is an important step. The problem addressed in this paper is that of enrichment of sensor descriptions and measurements in order to provide richer data, i.e., data containing more meaning. We propose a framework for automatizing the process of semantically enriching sensor descriptions and measurements with the purpose of improving the usability and accessibility of sensor data.

Metadata management for the web of things: a practical perspective

Motivated by the importance of metadata for WoT systems, in this paper, we describe building a metadata management system which is scalable and rich in semantics. We describe two implementation approaches and discuss advantages and disadvantages of each: the embedded approach and the middleware approach. We also identify three components relevant to managing the metadata: the storage, the representation and the access. Based on our experience with implementation, we conclude that: (i) both the embedded and the middleware solutions can already be prototyped, but some critical technologies for the embedded approach are still in early development and require considerable improvements, (ii) XML like syntax is not well suited for storing and transmission of metadata due to sensor device constraints with respect to available storage and link datarate; and (iii) the middleware approach proved more convenient from the web application developers point of view.

Supporting Rule Generation and Validation on Environmental Data in EnStreaM

Detection rules represent one of the components of the rule models in event processing systems. These rules can be discovered from data using data mining techniques or domain experts’ knowledge. We demonstrate a system that provides its users the means for creating and validating such rules. The system is applied on real-life environmental scenarios, where the main source of data comes from sensors. Based on historical data about events of interest, the scope is to formulate rules that could have caused these events. Using a scalable infrastructure the rules can be tested on massive amount of data in order to observe how past events would fit to these rules. In addition, we create semantic annotations of the dataset and use them in the system outputs in order to support interoperability with other systems.

Towards building a global oracle: a physical mashup using artificial intelligence technology

In this paper, we describe Videk - a physical mashup which uses artificial intelligence technology. We make an analogy between human senses and sensors; and between human brain and artificial intelligence technology respectively. This analogy leads to the concept of Global Oracle. We introduce a mashup system which automatically collects data from sensors. The data is processed and stored by SenseStream while the meta-data is fed into ResearchCyc. SenseStream indexes aggregates, performs clustering and learns rules which then it exports as RuleML. ResearchCyc performs logical inference on the meta-data and transliterates logical sentences. The GUI mashes up sensor data with SenseStream output, ResearchCyc output and other external data sources: GoogleMaps, Geonames, Wikipedia and Panoramio.