Sunil Choenni

On the barriers for local government releasing open data

Due to expected benefits such as citizen participation and innovation, the release of Public Sector Information as open data is getting increased attention on various levels of government. However, currently data release by governments is still novel and there is little experience and knowledge thus far about its benefits, costs and barriers. This is compounded by a lack of understanding about how internal processes influence data release. Our aim in this paper is to get a better understanding of these processes and how they influence data release, i.e., to find determinants for the release of public sector information. For this purpose, we conducted workshops, interviews, questionnaires, desk research and practice based cases in the education program of our university, involving six local public sector organizations. We find that the way data is stored, the way data is obtained and the way data is used by a department are crucial indicators for open data release. We conclude with the lessons learned based on our research findings. These findings are: we should take a nuanced approach towards data release, avoid releasing data for its own sake, and take small incremental steps to explore data release.

Index selection in relational databases

Intending to develop a tool which aims to support the physical design of relational databases can not be done without considering the problem of index selection. Generally the problem is split into a primary and secondary index selection problem and the selection is done per table. Whereas much attention has been paid on the selection of secondary indices relatively less is known about the selection of a primary index and the relation between them. These are exactly the topics of this paper.<<ETX>>

Exploring process barriers to release public sector information in local government

Due to expected benefits such as citizen participation and innovation, the release of Public Sector Information is getting increased attention on various levels of government. However, currently, data release by governments is still novel, with little experience and knowledge thus far about the benefits and barriers of release. This is compounded by a lack of understanding about how internal processes influence data release. Our aim in this paper is to get a better understanding of these processes and how they influence data release, i.e, to find determinants for the release of public sector information. For this purpose, we conducted workshops, interviews, questionnaires and desk research. We find that the way data is used by a department, the way data is obtained, how data is stored, and the suitability of data to become open, as crucial indicators for open data release. We conclude with lessons learned based on the research findings. These are that we should take a nuanced approach towards data release, avoid releasing data for its own sake and take small incremental steps to explore data release.

Who will watch (over) me? Humane monitoring in dementia care

The ageing population as well as the tight labor market put pressure on future health care. In this article, we explore the role of monitoring systems in small-scale housing for older people with dementia. By incorporating principles of value-sensitive design in a human centered design process we developed a system for remote monitoring at night in dementia care. The performance of the working system was evaluated in the real-life context of a nursing home and is currently being implemented in small-scale housing. Next to reporting the iterative design and evaluation of the monitoring system, we reflect upon the approach taken.

Issues and Guiding Principles for Opening Governmental Judicial Research Data

The opening of data is considered to provide many benefits. However, opening up data by public bodies is a complex and ill-understood activity. Although many public bodies might be willing to open up their data, they lack any systematic guidance. In this paper, guidance is provided by investigating the publishing processes at the Dutch Research and Documentation Centre (WODC), which owns governmental judicial research data. We developed guidance by providing 1) a list of issues that play a role in deciding whether to open data, 2) an alternative to completely publishing data (i.e. restricted access) and 3) solutions for overcoming some of the issues. The latter include dealing with privacy-sensitive data, deletion policies, publishing after embargo periods instead of not publishing at all, adding related documents and adding information about the quality and completeness of datasets. The institutional context should be taken into account when using the guidance, as opening data requires considerable changes of organizations.

Public safety mashups to support policy makers

To shape an effective and sound public safety policy, policy makers have a need for statistical insights in factors that may influence public safety. Today, there is a wide variety of sources available that contain data that is related to public safety. The data in these sources varies from register to survey data. There is a growing need to mash data from these sources and to create a coherent and uniform view of public safety. In this paper, we present a tool to create public safety mashups. The tool collects and processes safety related data from relevant sources. Mashups are presented in a uniform and accessible way to support policy makers in developing sound public safety policies. Besides data that is directly related to public safety, also contextual information, such as demographic data, is included in the tool that may help the user to understand the development of public safety aspects in the course of time.

Reconciling contradictions of open data regarding transparency, privacy, security and trust

While Open Data initiatives are diverse, they aim to create and contribute to public value. Yet several potential contradictions exist between public values, such as trust, transparency, privacy, and security, and Open Data policies. To bridge these contradictions, we present the notion of precommitment as a restriction of ones choices. Conceptualized as a policy instrument, precommitment can be applied by an organization to restrict the extent to which an Open Data policy might conflict with public values. To illustrate the use of precommitment, we present two case studies at two public sector organizations, where precommitment is applied during a data request procedure to reconcile conflicting values. In this procedure, precommitment is operationalized in three phases. In the first phase, restrictions are defined on the type and the content of the data that might be requested. The second phase involves the preparation of the data to be delivered according to legal requirements and the decisions taken in phase 1. Data preparation includes amongst others the deletion of privacy sensitive or other problematic attributes. Finally, phase 3 pertains to the establishment of the conditions of reuse of the data, limiting the use to restricted user groups or opening the data for everyone.

On the selection of optimal index configuration in OO databases

An operation in object-oriented databases gives rise to the processing of a path. Several database operations may result into the same path. The authors address the problem of optimal index configuration for a single path. As it is shown an optimal index configuration for a path can be achieved by splitting the path into subpaths and by indexing each subpath with the optimal index organization. The authors present an algorithm which is able to select an optimal index configuration for a given path. The authors consider a limited number of existing indexing techniques (simple index, inherited index, nested inherited index, multi-index, and multi-inherited index) but the principles of the algorithm remain the same adding more indexing techniques.<<ETX>>

Combining and Analyzing Judicial Databases

To monitor crime and law enforcement, databases of several organizations, covering different parts of the criminal justice system, have to be integrated. Combined data from different organizations may then be analyzed, for instance, to investigate how specific groups of suspects move through the system. Such insight is useful for several reasons, for example, to define an effective and coherent safety policy. To integrate or relate judicial data two approaches are currently employed: a data warehouse and a dataspace approach. The former is useful for applications that require combined data on an individual level. The latter is suitable for data with a higher level of aggregation. However, developing applications that exploit combined judicial data is not without risk. One important issue while handling such data is the protection of the privacy of individuals. Therefore, several precautions have to be taken in the data integration process: use aggregate data, follow the Dutch Personal Data Protection Act, and filter out privacy-sensitive results. Another issue is that judicial data is essentially different from data in exact or technical sciences. Therefore, data mining should be used with caution, in particular to avoid incorrect conclusions and to prevent discrimination and stigmatization of certain groups of individuals.

Trusted third parties for secure and privacy-preserving data integration and sharing in the public sector

For public organizations data integration and sharing are important in delivering better services. However, when sensitive data are integrated and shared, privacy protection and information security become key issues. This means that information systems must be secured and that access to sensitive data must be controlled. In this paper, a framework is presented to support data sharing between public organizations for collaboration purposes. The framework focuses on solutions towards optimal data sharing and integration while ensuring the security and privacy of individuals. Data sharing is based on the need-to-know principle, that is, data are only made available when they are required to perform core processes. To facilitate this, an approach is introduced in the form of a trusted third party that manages access control to personal information and thus helps to protect the privacy of individuals. It is argued that the proposed framework is suitable for data integration and sharing on various levels. An example of best practices of data sharing in the Netherlands shows how this framework facilitates data sharing to perform knowledge transfer and other higher-level tasks.