Search results

Mobile computing enables end‐users to create small services on their mobiles and share valuable and context‐aware information with others. The purpose of this paper is to introduce a platform for end‐user generated mobile services – so‐called microservices.

As a key component the authors present a microservice description language for user‐driven mobile service creation and platform‐independent service execution and rendering. The paper also gives insight into the authors' visual authoring tool. The chosen design approach is evaluated in two phases: an intermediate evaluation with a small hands‐on trial and an online survey; and a final laboratory test with 24 test users in total.

The paper provides empirical insights about the methods and motivations of end‐users creating small mobile services. The main purposes of service creation would be mostly to exchange information, stay in contact, and just for fun (on the basis of non‐commercial use). The evaluations also indicate the visual drag and drop approach of putting service blocks together as being the most favored in terms of user satisfaction.

The concepts and findings introduced in this paper will help in designing mobile service authoring environments, which is appealing to software communities/vendors and mobile network operators. The presented platform is, to the authors' knowledge, the first designed and implemented infrastructure enabling end‐user mobile service creation.

The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale.

The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre-processed by a Big Data streaming platform to lower the workload to be processed by the rule engine.

By creating a real-world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem.

In this article, real-world sensor data from only two buildings were extrapolated for the simulations. Obviously, real-world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work.

The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum.

The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.

The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.

The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.

Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.

Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.

This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.

This paper aims to integrate Big Data in e-government in Oman, also known as “e-Oman”, wherein Big Data might be better harnessed to tackle real-time challenges.

Besides a description of the concepts of e-government and Big Data in general, the paper underscores the dimensions of “e-Oman”. Following a qualitative approach, the paper asserts how integration of Big Data in “e-Oman” may be useful by invoking examples from four short case studies across different sectors.

The paper supports the integration of “e-Oman” and Big Data wherein besides providing smooth public services, the government is encouraged to forge inter- and intra-ministerial collaboration and public-private partnership. The paper probes through the challenges and opportunities in effecting this integration.

The paper provides a platform for the policymakers to conceive of a synchronized programme for integrating “e-Oman” and the Big Data generated by it. This integration would go a long way in building upon the economy of Oman, besides providing better public services to the individuals and businesses on a real-time basis.

The paper does throw light on the issues of privacy and confidentiality of data available with the government. There are challenges of cybercrime as well. Therefore, the paper posits that a robust fool-proof infrastructure should be instituted by the government for effecting integration of e-government and Big Data.

This paper seeks to fill the gap in extant literature which remains scant on the integration of e-government with Big Data. This is especially true in the case of Oman where not a single study has been presented to probe this issue. Given that “e-Oman” is expanding its scope over the years, this paper foresees the concomitant opportunities and challenges in the integration of Big Data in “e-Oman”.