Semi-automatic semantic enrichment of raw sensor data

One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management

Towards a Cloud-Based Service for Maintaining and Analyzing Data About Scientific Events

We propose the new cloud-based service OpenResearch for managing and analyzing data about scientific events such as conferences and workshops in a persistent and reliable way. This includes data about scientific articles, participants, acceptance rates, submission numbers, impact values as well as organizational details such as program committees, chairs, fees and sponsors. OpenResearch is a centralized repository for scientific events and supports researchers in collecting, organizing, sharing and disseminating information about scientific events in a structured way. An additional feature currently under development is the possibility to archive web pages along with the extracted semantic data in order to lift the burden of maintaining new and old conference web sites from public research institutions. However, the main advantage is that this cloud-based repository enables a comprehensive analysis of conference data. Based on extracted semantic data, it is possible to determine quality estimations, scientific communities, research trends as well the development of acceptance rates, fees, and number of participants in a continuous way complemented by projections into the future. Furthermore, data about research articles can be systematically explored using a content-based analysis as well as citation linkage. All data maintained in this crowd-sourcing platform is made freely available through an open SPARQL endpoint, which allows for analytical queries in a flexible and user-defined way.Comment: A completed version of this paper had been accepted in SAVE-SD workshop 2017 at WWW conferenc

Extracting Semantics of Individual Places from Movement Data by Analyzing Temporal Patterns of Visits

Data reflecting movements of people, such as GPS or GSM tracks, can be a source of information about mobility behaviors and activities of people. Such information is required for various kinds of spatial planning in the public and business sectors. Movement data by themselves are semantically poor. Meaningful information can be derived by means of interactive visual analysis performed by a human expert; however, this is only possible for data about a small number of people. We suggest an approach that allows scaling to large datasets reflecting movements of numerous people. It includes extracting stops, clustering them for identifying personal places of interest (POIs), and creating temporal signatures of the POIs characterizing the temporal distribution of the stops with respect to the daily and weekly time cycles and the time line. The analyst can give meanings to selected POIs based on their temporal signatures (i.e., classify them as home, work, etc.), and then POIs with similar signatures can be classified automatically. We demonstrate the possibilities for interactive visual semantic analysis by example of GSM, GPS, and Twitter data. GPS data allow inferring richer semantic information, but temporal signatures alone may be insufficient for interpreting short stops. Twitter data are similar to GSM data but additionally contain message texts, which can help in place interpretation. We plan to develop an intelligent system that learns how to classify personal places and trips while a human analyst visually analyzes and semantically annotates selected subsets of movement data

Trajectory Data Analysis in Support of Understanding Movement Patterns: A Data Mining Approach

Recent developments in wireless technology, mobility and networking infrastructures increased the amounts of data being captured every second. Data captured from the digital traces of moving objects and devices is called trajectory data. With the increasing volume of spatiotemporal trajectories, constructive and meaningful knowledge needs to be extracted. In this paper, a conceptual framework is proposed to apply data mining techniques on trajectories and semantically enrich the extracted patterns. A design science research approach is followed, where the framework is tested and evaluated using a prototypical instantiation, built to support decisions in the context of the Egyptian tourism industry. By applying association rule mining, the revealed time-stamped frequently visited regions of interest (ROI) patterns show that specific semantic annotations are required at early stages in the process and on lower levels of detail, refuting the presumption of cross-application usable patterns

Semantically enhancing multimedia lifelog events

Lifelogging is the digital recording of our everyday behaviour in order to identify human activities and build applications that support daily life. Lifelogs represent a unique form of personal multimedia content in that they are temporal, synchronised, multi-modal and composed of multiple media. Analysing lifelogs with a view to supporting content-based access, presents many challenges. These include the integration of heterogeneous input streams from different sensors, structuring a lifelog into events, representing events, and interpreting and understanding lifelogs. In this paper we demonstrate the potential of semantic web technologies for analysing lifelogs by automatically augmenting descriptions of lifelog events. We report on experiments and demonstrate how our re- sults yield rich descriptions of multi-modal, multimedia lifelog content, opening up even greater possibilities for managing and using lifelogs

Reusing Ontologies to Enrich Semantically User Content in Web2.0: A Case Study on Folksonomies

Semantic Web and Web2.0 emerged during the past decade promising to achieve new frontiers for the Web. On the one hand, the Semantic Web is an interlinked web of data, supported by ontological semantics and allowing for intelligent applications such as semantic search and integration of heterogeneous content across systems and applications. On the other hand, Web2.0 represents the new technologies and paradigms that revolutionised the user engagement in content creation and introduced novel means towards social interaction. Bridging the gap between Web2.0 and the Semantic Web has been proposed as a means to better manage and interact with the large amounts of user contributed content, which is a new challenge for Web2.0. This thesis focuses on a popular paradigm of Web2.0, folksonomies. In particular, we investigate the semantic enrichment of folksonomy tagspaces by reusing ontologies available in the Semantic Web. We identify the need for methods that automatically apply semantic descriptions to user generated content without requiring user intervention or alteration of the current tagging paradigm. We use an iterative approach in order to identify the characteristics of folksonomies and the attributes of knowledge sources that influence the semantic enrichment of tagspaces. We build on the results of our experimental studies to implement a folksonomy enrichment algorithm, that given an input tagspace, automatically creates a semantic structure that describes the meaning and relations of tags. We introduce measures for the evaluation of enriched tagspaces and finally, we propose a search algorithm that exploits the semantic structures to improve folksonomy search

Visual design recommendations for situation awareness in social media

The use of online Social Media is increasingly popular amongst emergency services to support Situational Awareness (i.e. accurate, complete and real-time information about an event). Whilst many software solutions have been developed to monitor and analyse Social Media, little attention has been paid on how to visually design for Situational Awareness for this large-scale data space. We describe an approach where levels of SA have been matched to corresponding visual design recommendations using participatory design techniques with Emergency Responders in the UK. We conclude by presenting visualisation prototypes developed to satisfy the design recommendations, and how they contribute to Emergency Responders’ Situational Awareness in an example scenario. We end by highlighting research issues that emerged during the initial evaluation