Josiane Xavier Parreira

A middleware framework for scalable management of linked streams

The Web has long exceeded its original purpose of a distributed hypertext system and has become a global, data sharing and processing platform. This development is confirmed by remarkable milestones such as the Semantic Web, Web services, social networks and mashups. In parallel with these developments on the Web, the Internet of Things (IoT), i.e., sensors and actuators, has matured and has become a major scientific and economic driver. Its potential impact cannot be overestimated-for example, in logistics, cities, electricity grids and in our daily life, in the form of sensor-laden mobile phones-and rivals that of the Web itself. While the Web provides ease of use of distributed resources and a sophisticated development and deployment infrastructure, the IoT excels in bringing real-time information from the physical world into the picture. Thus a combination of these players seems to be the natural next step in the development of even more sophisticated systems of systems. While only starting, there is already a significant amount of sensor-generated, or more generally dynamic information, available on the Web. However, this information is not easy to access and process, depends on specialised gateways and requires significant knowledge on the concrete deployments, for example, resource constraints and access protocols. To remedy these problems and draw on the advantages of both sides, we try to make dynamic, online sensor data of any form as easily accessible as resources and data on the Web, by applying well-established Web principles, access and processing methods, thus shielding users and developers from the underlying complexities. In this paper we describe our Linked Stream Middleware (LSM, http://lsm.deri.ie/), which makes it easy to integrate time-dependent data with other Linked Data sources, by enriching both sensor sources and sensor data streams with semantic descriptions, and enabling complex SPARQL-like queries across both dataset types through a novel query processing engine, along with means to mashup the data and process results. Most prominently, LSM provides (1) extensible means for real-time data collection and publishing using a cloud-based infrastructure, (2) a Web interface for data annotation and visualisation, and (3) a SPARQL endpoint for querying unified Linked Stream Data and Linked Data. We describe the system architecture behind LSM, provide details of how Linked Stream Data is generated, and demonstrate the benefits and efficiency of the platform by showcasing some experimental evaluations and the systems interface.

CityPulse: Large Scale Data Analytics Framework for Smart Cities

Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on peoples everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.

Hybrid SPARQL queries: fresh vs. fast results

For Linked Data query engines, there are inherent trade-offs between centralised approaches that can efficiently answer queries over data cached from parts of the Web, and live decentralised approaches that can provide fresher results over the entire Web at the cost of slower response times. Herein, we propose a hybrid query execution approach that returns fresher results from a broader range of sources vs. the centralised scenario, while speeding up results vs. the live scenario. We first compare results from two public SPARQL stores against current versions of the Linked Data sources they cache; results are often missing or out-of-date. We thus propose using coherence estimates to split a query into a sub-query for which the cached data have good fresh coverage, and a sub-query that should instead be run live. Finally, we evaluate different hybrid query plans and split positions in a real-world setup. Our results show that hybrid query execution can improve freshness vs. fully cached results while reducing the time taken vs. fully live execution.

Linked Stream Data Processing

Linked Stream Data has emerged as an effort to represent dynamic, time-dependent data streams following the principles of Linked Data. Given the increasing number of available stream data sources like sensors and social network services, Linked Stream Data allows an easy and seamless integration, not only among heterogenous stream data, but also between streams and Linked Data collections, enabling a new range of real-time applications.

Live linked open sensor database

There are millions of sensors being deployed all over the world. Data generated by these sensors is provided in different formats and interfaces and is rarely associated with semantics that describe its meaning. The heterogeneity and lack of semantic descriptions pose a big barrier for accessing sensor data and combining it with other data sources. The Live Linked Open Sensor Database project is the first project to provide a live database of semantically enriched sensor data, where each sensor reading is extended by adding proper metadata and by linking it to resources in the Linked Open Data Cloud. Currently, the database provides information of approximately 200,000 sensors and we are currently working on expanding it to incorporate even more data sources.

Freshening up while staying fast: towards hybrid SPARQL queries

Querying over cached indexes of Linked Data often suffers from stale or missing results due to infrequent updates and partial coverage of sources. Conversely, live decentralised approaches offer fresh results directly from the Web, but exhibit slow response times due to accessing numerous remote sources at runtime. We thus propose a hybrid query approach that improves upon both paradigms, offering fresher results from a broader range of sources than Linked Data caches while offering faster results than live querying. Our hybrid query engine takes a cached and live query engine as black boxes, where a hybrid query planner splits an input query and delegates the appropriate sub-queries to each interface. In this paper, we discuss query planning alternatives and their main strengths and weaknesses. We also present coherence measures to quantify the coverage and freshness for cached indexes of Linked Data, and show how these measures can be used for hybrid query planning to optimise the trade-off between fresh results and fast runtimes.

Multi-level Semantic Labelling of Numerical Values

With the success of Open Data a huge amount of tabular data sources became available that could potentially be mapped and linked into the Web of (Linked) Data. Most existing approaches to “semantically label” such tabular data rely on mappings of textual information to classes, properties, or instances in RDF knowledge bases in order to link – and eventually transform – tabular data into RDF. However, as we will illustrate, Open Data tables typically contain a large portion of numerical columns and/or non-textual headers; therefore solutions that solely focus on textual “cues” are only partially applicable for mapping such data sources. We propose an approach to find and rank candidates of semantic labels and context descriptions for a given bag of numerical values. To this end, we apply a hierarchical clustering over information taken from DBpedia to build a background knowledge graph of possible “semantic contexts” for bags of numerical values, over which we perform a nearest neighbour search to rank the most likely candidates. Our evaluation shows that our approach can assign fine-grained semantic labels, when there is enough supporting evidence in the background knowledge graph. In other cases, our approach can nevertheless assign high level contexts to the data, which could potentially be used in combination with other approaches to narrow down the search space of possible labels.

Semantic tagging of places based on user interest profiles from online social networks

In recent years, location based services (LBS) have become very popular. The performance of LBS depends on number of factors including how well the places are described. Though LBS enable users to tag places, users rarely do so. On the other hand, users express their interests via online social networks. The common interests of a group of people that has visited a particular place can potentially provide further description for that place. In this work we present an approach that automatically assigns tags to places, based on interest profiles and visits or check-ins of users at places. We have evaluated our approach with real world datasets from popular social network services against a set of manually assigned tags. Experimental results show that we are able to derive meaningful tags for different places and that sets of tags assigned to places are expected to stabilise as more unique users visit places.

Spatial Ontology-Mediated Query Answering over Mobility Streams

The development of (semi)-autonomous vehicles and communication between vehicles and infrastructure (V2X) will aid to improve road safety by identifying dangerous traffic scenes. A key to this is the Local Dynamic Map (LDM), which acts as an integration platform for static, semi-static, and dynamic information about traffic in a geographical context. At present, the LDM approach is purely database-oriented with simple query capabilities, while an elaborate domain model as captured by an ontology and queries over data streams that allow for semantic concepts and spatial relationships are still missing. To fill this gap, we present an approach in the context of ontology-mediated query answering that features conjunctive queries over DL-Lite\(_A\) ontologies allowing spatial relations and window operators over streams having a pulse. For query evaluation, we present a rewriting approach to ordinary DL-Lite\(_A\) that transforms spatial relations involving epistemic aggregate queries and uses a decomposition approach that generates a query execution plan. Finally, we report on experiments with two scenarios and evaluate our implementation based on the stream RDBMS PipelineDB.

Aspern smart ICT: Data analytics and privacy challenges in a smart city

The abundance of data in the context of smart cities yields huge potential for data-driven businesses but raises unprecedented challenges on data privacy and security. Some of these challenges can be addressed merely through appropriate technical measures, while other issues can only be solved through strategic organizational decisions. In this paper, we present few cases from a real smart city project. We outline some exemplary data analytics scenarios and describe the measures that we adopt for a secure handling of data. Finally, we show how the chosen solutions impact the awareness of the public and acceptability of the project.