Kenniswerkplaats Urban Big Data

Deel I
Titel: A Big Data Approach to Support Information Distribution in Crisis Response.
Abstract: Crisis response organisations operate in very dynamic environments, in which it is essential for responders to acquire all information critical to their task execution in time. In reality, the responders are often faced with information overload, incomplete information, or a combination of both. This hampers their decision-making process, workflow, situational awareness and, consequently, effective execution of collaborative crisis response. Therefore, getting the right information to the right person at the right time is of crucial importance.

The task of processing all data during crisis response situations and determining for whom at a particular moment the information is relevant is not straightforward. When developing an information system to support this task, some important challenges have to be taken into account. These challenges relate to the structure and truthfulness of the used data, the assessment of information relevance and the dissemination of relevant information in time. While methods and techniques from Big Data can be used to collect and integrate data, machine learning can be used to build a model for relevance assessments. An implementation of such a framework of Big Data is the TAID software system that collects and integrates data communicated between first responders and may send information to crisis responders that were not addressed in the initial communication. As an example of the impact of TAID on crisis response, we describe its effect in a simulated crisis response scenario.

Deel II
Title: Protecting privacy in Big Data settings
Abstract: Emergence and growth of IoT result in proliferation of large volumes of data in different types and formats, in a fast and dynamic pace. The Big Data paradigm is concerned with collection, storage and processing of such data that pave the way to a wide range of innovate applications. For example, refrigerators connected to grocery stores can automatically order goods whenever they sense low levels of grocery articles. Although Big Data offers various opportunities and prospects, it faces many challenges such as data quality and privacy. In this presentation we shall focus on the challenge of privacy preserving in Big Data settings, specially in the light of the recent European regulation (i.e., the General Data Protection Regulation) to be effective in a couple of years. We shall outline main privacy issues and (legal) requirements. The success of IoT and Big Data to become reality, we shall argue, depends on how we appropriately address the privacy challenge proactively (i.e., by adopting the privacy by design approach). To this end, we shall sketch some technical solutions directions.

Emergence and growth of IoT result in proliferation of large volumes of data in different types and formats, in a fast and dynamic pace. The Big Data paradigm is concerned with collection, storage and processing of such data that pave the way to a wide range of innovate applications. For example, refrigerators connected to grocery stores can automatically order goods whenever they sense low levels of grocery articles.

Although Big Data offers various opportunities and prospects, it faces many challenges such as data quality and privacy. In this presentation we shall focus on the challenge of privacy preserving in Big Data settings, specially in the light of the recent European regulation (i.e., the General Data Protection Regulation) to be effective in a couple of years. We shall outline main privacy issues and (legal) requirements. The success of IoT and Big Data to become reality, we shall argue, depends on how we appropriately address the privacy challenge proactively (i.e., by adopting the privacy by design approach). To this end, we shall sketch some technical solutions directions.

Speaker:
Niels Netten, Tony Busker, Mortaza Bargh