AN ENTITY-CENTRIC APPROACH TO GREEN INFORMATION SYSTEMS

The integration of sustainable thinking and performance within day-to-day business activities has become an important business need. Sustainable business requires information on the use, flows and destinies of energy, water, and materials including waste, along with monetary information on environment-related costs, earnings, and savings. Creating this holistic view of economic, social and environmental information is not a straightforward mission from an IT perspective, and implies tackling several challenges such as information granularity and overload, the different projections of the same factual information, and the heterogeneity of information systems. In this paper, we propose an entity-centric approach to Green Information Systems to assist organisations in forming a cohesive representation of the environmental impact of their business operations at both micro- and macrolevels. Initial results from a Small Medium-size Enterprise case study are discussed along with future research directions

Automatic Anomaly Detection over Sliding Windows: Grand Challenge

With the advances in the Internet of Things and rapid generation of vast amounts of data, there is an ever growing need for leveraging and evaluating event-based systems as a basis for building realtime data analytics applications. The ability to detect, analyze, and respond to abnormal patterns of events in a timely manner is as challenging as it is important. For instance, distributed processing environment might affect the required order of events, time-consuming computations might fail to scale, or delays of alarms might lead to unpredicted system behavior. The ACM DEBS Grand Challenge 2017 focuses on real-time anomaly detection for manufacturing equipments based on the observation of a stream of measurements generated by embedded digital and analogue sensors. In this paper, we present our solution to the challenge leveraging the Apache Flink stream processing framework and anomaly ordering based on sliding windows, and evaluate the performance in terms of event latency and throughput

Internet of Things Enhanced User Experience for Smart Water and Energy Management

Smart environments can engage a wide range of end users with different interests and priorities, from corporate managers looking to improve the performance of their business to school children who want to explore and learn more about the world around them. Creating an effective user experience within a smart environment (from smart buildings to smart cities) is an important factor to success. In this article, we reflect on our experience of developing Internet-of-Things-enabled applications within a smart home, school, office building, university, and airport, where the goal has been to engage a wide range of users (from building managers to business travelers) to increase water and energy awareness, management, and conservation

Technical Research Priorities for Big Data

To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data

Automatic Anomaly Detection over Sliding Windows: Grand Challenge

With the advances in the Internet of Things and rapid generation of vast amounts of data, there is an ever growing need for leveraging and evaluating event-based systems as a basis for building realtime data analytics applications. The ability to detect, analyze, and respond to abnormal patterns of events in a timely manner is as challenging as it is important. For instance, distributed processing environment might affect the required order of events, time-consuming computations might fail to scale, or delays of alarms might lead to unpredicted system behavior. The ACM DEBS Grand Challenge 2017 focuses on real-time anomaly detection for manufacturing equipments based on the observation of a stream of measurements generated by embedded digital and analogue sensors. In this paper, we present our solution to the challenge leveraging the Apache Flink stream processing framework and anomaly ordering based on sliding windows, and evaluate the performance in terms of event latency and throughput

Grand challenge: Automatic anomaly detection over sliding windows

With the advances in the Internet of Things and rapid generation of vast amounts of data, there is an ever growing need for leveraging and evaluating event-based systems as a basis for building realtime data analytics applications. The ability to detect, analyze, and respond to abnormal patterns of events in a timely manner is as challenging as it is important. For instance, distributed processing environment might affect the required order of events, time-consuming computations might fail to scale, or delays of alarms might lead to unpredicted system behavior. The ACM DEBS Grand Challenge 2017 focuses on real-time anomaly detection for manufacturing equipments based on the observation of a stream of measurements generated by embedded digital and analogue sensors. In this paper, we present our solution to the challenge leveraging the Apache Flink stream processing framework and anomaly ordering based on sliding windows, and evaluate the performance in terms of event latency and throughput

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

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

Automatic Anomaly Detection over Sliding Windows: Grand Challenge

With the advances in the Internet of Things and rapid generation of vast amounts of data, there is an ever growing need for leveraging and evaluating event-based systems as a basis for building realtime data analytics applications. The ability to detect, analyze, and respond to abnormal patterns of events in a timely manner is as challenging as it is important. For instance, distributed processing environment might affect the required order of events, time-consuming computations might fail to scale, or delays of alarms might lead to unpredicted system behavior. The ACM DEBS Grand Challenge 2017 focuses on real-time anomaly detection for manufacturing equipments based on the observation of a stream of measurements generated by embedded digital and analogue sensors. In this paper, we present our solution to the challenge leveraging the Apache Flink stream processing framework and anomaly ordering based on sliding windows, and evaluate the performance in terms of event latency and throughput

Grand challenge: Automatic anomaly detection over sliding windows

With the advances in the Internet of Things and rapid generation of vast amounts of data, there is an ever growing need for leveraging and evaluating event-based systems as a basis for building realtime data analytics applications. The ability to detect, analyze, and respond to abnormal patterns of events in a timely manner is as challenging as it is important. For instance, distributed processing environment might affect the required order of events, time-consuming computations might fail to scale, or delays of alarms might lead to unpredicted system behavior. The ACM DEBS Grand Challenge 2017 focuses on real-time anomaly detection for manufacturing equipments based on the observation of a stream of measurements generated by embedded digital and analogue sensors. In this paper, we present our solution to the challenge leveraging the Apache Flink stream processing framework and anomaly ordering based on sliding windows, and evaluate the performance in terms of event latency and throughput.non-peer-reviewe