Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.     

Path Flow and Trip Matrix Estimation Using Link Flow Density

A macroscopic model is presented that simultaneously estimates route flows and trip matrices for congested road networks using data on link densities instead of link flows. The advantage of this approach is that it avoids errors that may occur in the individual links’ flow-cost relationships when congestion is heavy. Under the proposed methodology, both the flows and the matrices are estimated by the model using an image of the network such as an aerial photograph in which the number of vehicles on each link can be identified. The model itself is formulated as a maximum entropy optimization problem subject to linear constraints given by vehicle densities on the links, and is validated using analytic examples and traffic microsimulations. The results demonstrate the superiority of the link-density approach over the traditional flow-based method.     

Evolutionary computing in recommender systems: a review of recent research

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.     

A versatile technology platform for microfluidic handling systems,part II: channel design and technology

Microfluidic devices often require channels of a specific size and shape. These devices are then made in a fabrication process that is often specialized to produce only those (and very similar) channels. As a result, devices requiring channels of different size and shape cannot easily be integrated on the same chip. This paper presents a method to fabricate microfluidic channels in a wide range of shape and size on the same chip by using a slit pattern through which the channels are etched. The fabrication process to fabricate these channels is discussed in detail, and an empirical model is presented to find the optimal slit pattern for a required size and shape. This part of the paper focusses on the channel design and fabrication. Details on the whole fabrication process and optional functionalization of the channels are presented in part I of this paper.     

Local closed world reasoning with description logics under the well-founded semantics

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases (Motik and Rosati, 2010 [52]), and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.     

Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

Analysis of monotonic greening and browning trends from global NDVI time-series

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.     

Stability analysis in spanwise-periodic double-sided lid-driven cavity flows with complex cross-sectional profiles

Three-dimensional linear instability analyses are presented of steady two-dimensional laminar flows in the lid-driven cavity defined by [15] and further analyzed in the present volume [1], as well as in a derivative of the same geometry. It is shown that in both of the geometries considered three-dimensional BiGlobal instability leads to deviation of the flow from the two-dimensional solution; the analysis results are used to define low- and high-Reynolds number solutions by reference to the flow physics. Critical conditions for linear global instability and neutral loops are presented in both geometries.     

A fast MATLAB program to estimate the multifractal spectrum of multidimensional data: Application to fractures

A MATLAB^® program based on the Hou algorithm for estimation of fractal dimension and multifractal spectrum of fractures is presented. The program performance was tested with many synthetical fractals and field data. Interpolation and sampling effects on the fractal dimension and multifractal spectrum estimation were also studied. Some common problems related to the fractal dimension and multifractal spectrum are also discussed.