Wassim Derguech

Using Formal Concept Analysis for Organizing and Discovering Sensor Capabilities

Smart environments rely on sensor data to provide necessary business intelligence in order to support decision-making. An efficient decision support model in such a context requires that sensor data are provided correctly and timely. Given the dynamicity of sensor environment, the diversity of their features and of user requirements, finding appropriate sensors having the required capabilities or replacing faulty ones constitutes a challenging task. Efficiently describing and organizing sensors in smart environments is essential to deliver a rapid adaptation to errors and availability of data. In this paper, we present an approach for organizing and indexing sensor services based on their capabilities. We introduce a feature-oriented capability model that puts forward the functional aspects of carried actions and model them as resource description framework (RDF) properties rather than focusing on the change in the state of the world. Using this model for describing sensor capabilities, we apply Formal Concept Analysis for organizing and indexing sensor services. We have experimented and evaluated our approach in the Digital Enterprise Research Institute, which has been retrofitted with various sensors to monitor temperature, motion, light and consumption of power within a building.

On-the-fly generation of multidimensional data cubes for web of things

The dynamicity of sensor data sources and publishing real-time sensor data over a generalised infrastructure like the Web pose a new set of integration challenges. Semantic Sensor Networks demand excessive expressivity for efficient formal analysis of sensor data. This article specifically addresses the problem of adapting data model specific or context-specific properties in automatic generation of multidimensional data cubes. The idea is to generate data cubes on-the-fly from syntactic sensor data to sustain decision making, event processing and to publish this data as Linked Open Data.

An Autonomic Approach to Real-Time Predictive Analytics Using Open Data and Internet of Things

Public datasets are becoming more and more available for organizations. Both public and private data can be used to drive innovations and new solutions to various problems. The Internet of Things (IoT) and Open Data are particularly promising in real time predictive data analytics for effective decision support. The main challenge in this context is the dynamic selection of open data and IoT sources to support predictive analytics. This issue is widely discussed in various domains including economics, market analysis, energy usage, etc. Our case study is the prediction of energy usage of a building using open data and IoT. We propose a two-step solution: (1) data management: collection, filtering and warehousing and (2) data analytics: source selection and prediction. This work has been evaluated in real settings using IoT sensors and open weather data.

An automation support for creating configurable process models

Configurable process models are constructed via the aggregation of several process models. Manual creation of configurable process models is tedious, time consuming and error prone task. We propose in this paper an automation support for creating these models. The contribution of this paper is a merging algorithm for integrating a set of process variants into a single configurable process model. This integrated process model should (i) subsume the behaviours of all original models, (ii) ensure a trace back of the origin of each element and (iii) derive any of the input models by means of configuration and individualization. Existing solutions either fail in respecting all these requirements or allow for merging only pairs of process models. However, our algorithm allows for merging a set of process models at once. This algorithm has been implemented and tested over a set of different process variants.

Semantically-enabled business process models discovery

Business process (BP) model discovery is a pillar technique for BP model reuse. Current discovery approaches cannot deal with the functional granularity gap that exists between BP models on the one hand, and BP queries on the other hand. In addition, they focus only on the structural dimension and do not consider functional and non-functional requirements. In this paper, we present an approach for BP model discovery which resolves the above problems. We present an RDF vocabulary which captures functional and non-functional aspects in addition to the control flow perspective. Having the functional and non-functional descriptions of basic activities, we use a set of algorithms to compute those of structured activities and therefore derive different representations of the same BP model at different granularity levels. Using a set of extraction rules, we build an RDF knowledge base that can be interrogated using SPARQL.

IoT-Enhanced User Experience for Smart Water and Energy Management

The two articles in this special section represent ongoing Internet of Things applications in the context of Europe trying to make solutions usable to people in daily times.

Designing business capability-aware configurable process models

Abstract Process Aware Information Systems manage processes within organisations on the basis of business process models. These models can be created either from scratch or by reusing exiting reference process models. Particular types of reference models are configurable process models that are created by merging multiple models into a single one that can be customized to the needs of the business experts. Using those models presents two main challenges: their creation and their configuration. In this paper, we focus on the first challenge and propose a novel algorithm for merging process models into a configurable process model. The difference in our work is the pre-annotated process models with their business capabilities that report on what actions each process element achieves. Our algorithm generates configurable models that are also annotated with their capabilities that can be used to face the second challenge of these models: the configuration phase. We tested our algorithm using real-world process models to evaluate the required creation time and resulting compression rate after merging the input models. The results show that the models can be created in few milliseconds and achieving a compression rate of 50%. We further carried out interviews with domain experts to assess the usefulness and the level of maturity of this work. The results show the importance of the automation of process merging using a tool support that we proposed. However, further adaptation efforts are required to integrate this work in the working environments of the interviewed experts.

Organizing Capabilities Using Formal Concept Analysis

In this paper, we discuss the importance of the concept of capability for describing what an action does from a functional perspective. We introduce a conceptual model for representing capabilities as attribute features entities. Furthermore, we use Formal Concept Analysis to create concept lattices for indexing a repository of capabilities descriptions. We applied this approach on a set of sensor capabilities deployed within the Linked Energy Intelligence dataspace.

A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments

Abstract As smart environments move from a research vision to concrete manifestations in real-world enabled by the Internet of Things, they are encountering a number of very practical challenges in data management in terms of the flexibility needed to bring together contextual and real-time data, the interface between new digital infrastructures and existing information systems, and how to easily share data between stakeholders in the environment. Therefore, data management approaches for smart environments need to support flexibility, dynamicity, incremental change, while keeping costs to a minimum. A Dataspace is an emerging approach to data management that has proved fruitful for personal information and scientific data management. However, their use within smart environments and for real-time data remains largely unexplored. This paper introduces a Real-time Linked Dataspace (RLD) as an enabling platform for data management within smart environments. This paper identifies common data management requirements for smart energy and water environments, details the RLD architecture and the key support services and their tiered support levels, and a principled approach to “Pay-As-You-Go” data management. The paper presents a dataspace query service for real-time data streams and entities to enable unified entity-centric queries across live and historical stream data. The RLD was validated in 5 real-world pilot smart environments following the OODA (Observe, Orient, Decide, and Act) Loop to build real-time analytics, decisions support, and smart apps for energy and water management. The pilots demonstrate that the RLD enables incremental pay-as-you-go data management with support services that simplify the development of applications and analytics for smart environments. Finally, the paper discusses experiences, lessons learnt, and future directions.

Using Ontologies for Business Capability modelling: Describing What Services and Processes Achieve

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