This paper presents STAR-CITY, a system supporting semantic traffic analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. We present how semantic diagnosis and predictive reasoning, both using and interpreting semantics of data to deliver useful, accurate and consistent inferences, have been exploited and adapted systematized in an intelligent user interface. Our prototype of semantics-aware traffic analytics and reasoning, experimented in Dublin City Ireland, works and scales efficiently with historical together with real live and heterogeneous stream data.

Many databases today are text-rich, comprising not only structured, but also textual data. Querying such databases involves predicates matching structured data combined with string predicates featuring textual constraints. Based on selectivity estimates for these predicates, query processing as well as other tasks that can be solved through such queries can be optimized. Existing work on selectivity estimation focuses either on string or on structured query predicates alone. Further, probabilistic models proposed to incorporate dependencies between predicates are focused on the relational setting. In this work, we propose a template-based probabilistic model, which enables selectivity estimation for general graph-structured data. Our probabilistic model allows dependencies between structured data and its text-rich parts to be captured. With this general probabilistic solution, BN+, selectivity estimations can be obtained for queries over text-rich graph-structured data, which may contain structured and string predicates (hybrid queries). In our experiments on real-world data, we show that capturing dependencies between structured and textual data in this way greatly improves the accuracy of selectivity estimates without compromising the efficiency.

Structured data representing entity descriptions often lacks precise type information. That is, it is not known to which type an entity belongs to, or the type is too general to be useful. In this work, we propose to deal with this novel problem of inferring the type semantics of structured data, called typification. We formulate it as a clustering problem and discuss the features needed to obtain several solutions based on existing clustering solutions. Because schema features perform best, but are not abundantly available, we propose an approach to automatically derive them from data. Optimized for the use of schema features, we present TYPifier, a novel clustering algorithm that in experiments, yields better typification results than the baseline clustering solutions.

The advent of real-time traffic streaming offers users the opportunity to visualise current traffic conditions and congestion information. However, real-time information highlighting the underlying reason for tail-backs remains largely unexplored. Broken traffic lights, an accident, a large concert, or road-works reveal important information for citizens and traffic operators alike. Providing such information in real-time requires intelligent mechanisms and user interfaces in order to (i) harness heterogeneous data sources (volume, velocity, variety, veracity) and (ii) make derived knowledge consumable so users can visualize traffic conditions and congestion information making better routing decisions while travelling. This work focuses on surfacing relevant information and explaining the underlying reasons behind traffic conditions. To this end, static data from event providers, planned road works together with dynamically emerging events such as a traffic accidents, localized weather conditions or unplanned obstructions are captured through social media to provide users real-time feedback to highlight the causes of traffic congestion.

Today plenty of data is emerging from various city systems. Beyond the classical Web resources, large amounts of data are retrieved from sensors, devices, social networks, governmental applications, or service networks. In such a diversity of information, answering specific information needs of city inhabitants requires holistic IR techniques, capable of harnessing different types of city data and turned it into actionable insights to answer different queries. This tutorial will present deep insights, challenges, opportunities and techniques to make heterogeneous city data searchable and show how emerging IR techniques models can be employed to retrieve relevant information for the citizens.

Cities today have become highly dense, dynamic living areas for the majority of planet's population and also focal points of innovation, commerce, and growth in a highly modernized world. Due to its intensifying importance, cities need to transform into sustainable, smarter and credible places to enable a tenantable and comfortable life for their citizens. City data, which is the source of our digitized knowledge about the cities, is a highly important element to achive this goal as it is the main input to build complex city ecosystems and to solve particular problems that are encountered in the cities today. In this respect, this workshop will provide a major forum to identify the challenges and opportunities in terms of better managing city data and to reveal its discriminating importance in various applications in a city ecosystem. As city data becomes more widespread and prevailing, it poses novel research problems which importantly are open to the investigation of a broad community of researchers in various fields.

Keyword query processing over structured data has gained a lot of interest as keywords have proven to be an intuitive mean for accessing complex results in databases. While there is a large body of work that provides different mechanisms for computing keyword search results efficiently, a recent study has shown that the problem of ranking is much neglected. Existing strategies employ heuristics that perform only in ad-hoc experiments but fail to consistently and repeatedly deliver results across different information needs. We provide a principled approach for ranking that focuses on a well-established notion of what constitutes relevant keyword search results. In particular, we adopt relevance-based language models to consider the structure and semantics of keyword search results, and introduce novel strategies for smoothing probabilities in this structured data setting. Using a standardized evaluation framework, we show that our work largely and consistently outperforms all existing systems across datasets and various information needs.

The evolution of service-oriented architectures towards digital ecosystems comprehends a number of challenges. According to Papazoglou et al., one challenge is to develop services in a dynamic environment with high uncertainties, and in collaboration with other companies. The Integrated Service Engineering (ISE) Workbench is a step in this direction in that it supports a model-driven approach and different stakeholders. This paper briefly introduces the ISE Framework, presents the ISE Workbench and its functionality, and proposes a demonstration of a conducted case study in the IT outsourcing domain.

Currently, clinical information is stored in all kinds of proprietary formats through a multitude of medical information systems available on the market. This results in a severe interoperability problem in sharing electronic healthcare records. To address this problem, an industry initiative, called "Integrating Healthcare Enterprise (IHE)" has specified the "Cross Enterprise Document Sharing (XDS)" Profile to store healthcare documents in an ebXML registry/ repository to facilitate their sharing. Through a separate effort, IHE has also defined interdepartmental Workflow Profiles to identify the transactions required to integrate information flow among several information systems. Although the clinical documents stored in XDS registries are obtained as a result of executing these workflows, IHE has not yet specified collaborative healthcare processes for the XDS. Hence, there is no way to track the workflows in XDS and the clinical documents produced through the workflows are manually inserted into the registry/ repository. Given that IHE XDS is using the ebXML architecture, the most natural way to integrate IHE Workflow Profiles to IHE XDS is using ebXML Business Processes (ebBP). In this paper, we describe the implementation of an enhanced IHE architecture demonstrating how ebXML Business Processes, IHE Workflow Profiles and the IHE XDS architecture can all be integrated to provide collaborative business process support in the healthcare domain.