May / June 2014

Cloud-Link
Cloud-Link

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Special Issue on Big Data (II)

This issue of Cloud-Link is the second of two about big data, including data-intensive science.  The first issue (March/April 2014) focused on methods and tools for making data available and analyzing them, as well as applications in medicine and some of the associated concerns.  This issue focuses on applications and case studies, “Internet of Things” and “smart cities,” societal impact, as well as data quality issues and legal issues.

In both the articles: “Big Data's Big Unintended Consequences” and “Who's Afraid of Big Data?”, the authors discuss potential issues or problems that might arise concerning the use of big data. They reflect the need for developing mechanisms or frameworks to govern the use of big data. The article “Innovation as the Strategic Driver of Sustainability: Big Data Knowledge for Profit and Survival” considers big data from a business perspective. The authors discuss how big data analytics can be used to foster innovation and hence enhance sustainability. On the other hand, both the articles “Bootstrapping Smart Cities through a Self-Sustainable Model Based on Big Data Flows” and “Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips”consider the use of big data from a city/urban perspective. The former article studies the use of big data for smart cities whereas the later article studies a specific data set on taxi trips. Last but not least, the article “On Optimal Scheduling in Duty-Cycled Industrial IoT Applications Using IEEE802.15.4e TSCH” studies big data flows for Internet of Things, focusing on a traffic aware scheduling algorithm.

We hope that these two issues of Cloud-Link can provide you with useful references to explore this important and interesting topic further. Articles have been selected based on various considerations (e.g., variety, relevancy, anticipated readers' interests) and unavoidably there are many other useful and insightful articles that have not been included. You are also encouraged to search through IEEE Xplore and other databases for further reading.

The next issue (July/August 2014) of Cloud-Link will be “Data Centre Networks”. If you would like to recommend any useful articles, please email them to hcbchan@ieee.org. Furthermore, we are looking for topics for the upcoming issues. If you have any suggestions, please also let us know.

Henry Chan, Victor Leung, Jens Jensen and Tomasz Wiktor Wlodarczyk
Editor and Associate Editors

Articles in this issue

Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips

By Ferreira, N.; Poco, J.; Vo, H.T.; Freire, J. and Silva, C.T.

Published in IEEE Transactions on Visualization and Computer Graphics, December 2013

As increasing volumes of urban data are captured and become available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based decision making and policies. In this paper, we focus on a particularly important urban data set: taxi trips. Taxis are valuable sensors and information associated with taxi trips can provide unprecedented insight into many different aspects of city life, from economic activity and human behavior to mobility patterns. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses (e.g., compare different regions over time). This problem is compounded due to the size of the data-there are on average 500,000 taxi trips each day in NYC. We propose a new model that allows users to visually query taxi trips. Besides standard analytics queries, the model supports origin-destination queries that enable the study of mobility across the city. We show that this model is able to express a wide range of spatio-temporal queries, and it is also flexible in that not only can queries be composed but also different aggregations and visual representations can be applied, allowing users to explore and compare results. We have built a scalable system that implements this model which supports interactive response times; makes use of an adaptive level-of-detail rendering strategy to generate clutter-free visualization for large results; and shows hidden details to the users in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and economists that show how our model and system enable domain experts to perform tasks that were previously unattainable for them.

Read the full article at IEEE Xplore...   Back to top

Who's Afraid of Big Data?

By Laplante, P. A.

Published in IT Professional, October 2013

Recently, there has been a great deal of news in the international media pertaining to various disclosures that governments--the US government, in particular--are collecting vast amounts of information from diverse sources in the interest of counterterrorism. Telephone information is being recorded in unprecedented volumes, and surface mail is being scanned. Furthermore, the movement of vehicles can be tracked by toll transponders, GPS, black boxes, and imaging devices. What's even more disturbing is that much of this information is probably being kept for later analysis. Using Google technology, this information can be indexed and mined for all kinds of transgressions, beyond scanning for terrorist-threat indicators or conducting forensic analysis of terrorist acts.

Read the full article at IEEE Xplore...   Back to top

On Optimal Scheduling in Duty-Cycled Industrial IoT Applications Using IEEE802.15.4e TSCH

By Palattella, M.R.; Accettura, N.; Grieco, L.A.; Boggia, G.; Dohler, M. and Engel, T.,

Published in IEEE Sensors Journal, October 2013

As exposed in a recent report by General Electric, an industrial Internet of Things (IoT) is emerging as a commercially viable embodiment of the IoT where physical sensors gather data readings from the field and deliver the traffic to the Internet. The collected real-time big data, in turn, allow the optimizing of entire industry verticals with enormous return of investments. Although opportunities are ample, it comes along with serious engineering design challenges as industrial applications have stringent requirements on delay, lifetime and standards-compliance. To this end, we advocate the use of an IEEE/IETF standardized IoT architecture along with a recently introduced data-centric scheduling algorithm known as traffic aware scheduling algorithm (TASA). Applying graph theoretical tools to the multi-channel, time-synchronized, and duty-cycled nature of TASA, we rigorously derive optimality and bounds on the minimum number of needed active slots (impacting end-to-end delays) and the network duty-cycle (impacting lifetime). We demonstrate the enormous superiority of TASA over traditional IEEE802.15.4/ZigBee approaches in terms of energy efficiency. The outcome of this paper is currently to lay foundations of the recently formed IETF standardization group 6TSCH with the aim to significantly improve IoT data flows over IEEE802.15.4e TSCH and IETF 6LoWPAN/ROLL enabled technologies.

Read the full article at IEEE Xplore...   Back to top

Innovation as the Strategic Driver of Sustainability: Big Data Knowledge for Profit and Survival

By Jelinek, M. and Bergey, P.

Published in IEEE Engineering Management Review, Second Quarter of 2013

Innovation has long been a central strategic focus of firms, and sustainability has recently become such a focus. We posit that innovation-across the value chain, in strategy, and in business models-is the central element of any truly sustainable business. Linking the theoretical models of Market Orientation (MO) and the Resource Based View of the Firm (RBV), purposive search directed through a Knowledge Based View (KBV) offers a schematic outline for how and where applications of big data analytics can facilitate innovation for long-term sustainability of the firm-for survival, profit, and dynamic fit with the changing environment.

Read the full article at IEEE Xplore...   Back to top

Bootstrapping Smart Cities through a Self-sustainable Model based on Big Data Flows

By Vilajosana, I.; Llosa, J.; Martinez, B.; Domingo-Prieto, M.; Angles, A. and Vilajosana, X.

Published in IEEE Communications Magazine, June 2013

We have a clear idea today about the necessity and usefulness of making cities smarter, the potential market size, and trials and tests. However, it seems that business around Smart Cities is having difficulties taking off and is thus running short of projected potentials. This article looks into why this is the case and proposes a procedure to make smart cities happen based on big data exploitation through the API stores concept. To this end, we first review involved stakeholders and the ecosystem at large. We then propose a viable approach to scale business within that ecosystem. We also describe the available ICT technologies and finally exemplify all findings by means of a sustainable smart city application. Over the course of the article, we draw two major observations, which are seen to facilitate sustainable smart city development. First, independent smart city departments (or the equivalent) need to emerge, much like today's well accepted IT departments, which clearly decouple the political element of the improved city servicing from the underlying technologies. Second, a coherent three-phase smart city rollout is vital, where in phase 1 utility and revenues are generated; in phase 2 only-utility service is also supported; and in phase 3, in addition, a fun/leisure dimension is permitted.

Read the full article at IEEE Xplore...   Back to top

Big Data's Big Unintended Consequences

By Wigan, M.R. and Clarke, R.

Published in IEEE Computer, June 2013

Businesses and governments exploit big data without regard for issues of legality, data quality, disparate data meanings, and process quality. This often results in poor decisions, with individuals bearing the greatest risk. The threats harbored by big data extend far beyond the individual, however, and call for new legal structures, business processes, and concepts such as a Private Data Commons. The Web extra at http://youtu.be/TvXoQhrrGzg is a video in which author Marcus Wigan expands on his article "Big Data's Big Unintended Consequences" and discusses how businesses and governments exploit big data without regard for issues of legality, data quality, disparate data meanings, and process quality. This often results in poor decisions, with individuals bearing the greatest risk. The threats harbored by big data extend far beyond the individual, however, and call for new legal structures, business processes, and concepts such as a Private Data Commons.

Read the full article at IEEE Xplore...   Back to top

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