Data paper on soccer-logs

Whether it is sunrise, the weekend, or Christmas, some form of temporal structure or periodic pattern governs our daily activities. Understanding them is essential to making sense of human activity, because they frame normality and allow us to identify abnormalities. However, cultural heterogeneity and scale greatly complicate our ability to uncover and understand human activity at a given time within a region. Current research in the field of visual analytics and geography provide methods of addressing spatiotemporal periodicity, but they fall short in providing access to multiple spatial and temporal scales via a relevant calendar. In response to these shortcomings, we developed PerSE (periodicity in spatiotemporal events), a coordinated-view Web application designed to aid users in the detection and analysis of calendar-related periodicity in spatiotemporal event data sets. Given the complexity of such a visualization tool, this paper focuses on the usability and learnability of PerSE. We evaluated the tool through a 20-participant study that consisted of training, a multiple-choice test, and the System Usability Scale. Our analysis of the results shows that the complex combination of visual tools and multi-scale, multi-calendar capability used within PerSE is effective, but still in need of usability improvements.

The immense growth of the social Web, which has made a large amount of user data easily and publicly available, has opened a whole new spectrum for research in social behavioral sciences. However, as the volume of social media content increases at a very fast rate, it becomes extremely difficult to systematically obtain high-level information from this data. As a consequence, tasks related to the analysis of historical news events based on social media data have not been explored, which limits any type of comparative historical research, causality analysis, and discovery of knowledge from patterns extracted from aggregated social media event information.In this work, we target this issue by proposing a compact high-level representation of news events using social media information. This representation explicitly includes temporal information about the event and information about locations, in particular of geopolitical entities. We call this a spatio-temporal context-aware event representation. Our hypothesis is that by including social, temporal, and spatial information in the event representation, we are enabling the analysis of historical world news from a social and geopolitical perspective. This facilitates, new information retrieval tasks related to historical event information extraction and international relations analysis. We support our claims by presenting two applications of this idea: the first, a visual tool, named Galean, for retrieval and exploration of historical news events within their geopolitical and temporal context. The second, a quantitative analysis of a 2-year Twitter dataset of news events reported by U.S. and U.K. media, which we explore using data mining techniques on our event representations. We present two case studies of event exploration using Galean and user evaluation of this tool, as well as details of our data mining empirical results.

Portable devices such as GPS-equipped smart phones and cameras are able to provide detailed spatio-temporal trip event data for each user. Such data can be aggregated over many users to provide large amounts of behavioral data of very fine granularity. Trying to simplify this data into meaningful higher-level insights is challenging for a variety of reasons. In this paper we study the problem of simplifying spatio-temporal trip data and summarizing them into an easily interpretable graph/network. We propose several constrained coupled nonnegative matrix factorization formulations that simultaneously cluster locations and times based on the associated trips, and develop a (block) coordinate descent algorithm to solve them. We empirically evaluate our approach on a real world data set of taxis' GPS traces and show the advantages of our approach over traditional clustering algorithms.

Diabetes mellitus performance measures in individuals with limited life expectancy.

Performance measurement and evaluation systems are among the most common management instruments. An integral element of this process is the use of targets, typically set in appraisal interviews and formalized via written target agreements. In this paper, we investigate the relationship between performance management and evaluation systems and individual effort, proxied by the commonly used concept of work engagement. Using four waves of a new representative, linked employer-employee data set, the Linked Personnel Panel (LPP), we apply fixed effects estimations to account for unobserved heterogeneity. Our results show positive and statistically significant relationships between the presence of a performance management and evaluation process and employee engagement on the individual level. We are further able to differentiate between appraisal interviews and written target agreements which allows us to show a positive effect of appraisal interviews and an additional positive effect of target agreements. In addition, we find first evidence that these direct relationships are partially mediated by goal clarity and procedural fairness.

A spatiotemporally referenced event is a tuple that contains both a spatial reference and a temporal reference. The spatial reference is typically a point coordinate, and the temporal reference is a timestamp. The event payload can be the reading of a sensor (IoT systems), a user comment (geo-tagged social networks), a news article (gdelt), etc. Spatiotemporal event datasets are ever growing, and the requirements for their processing goes beyond traditional client-sever GIS architectures. Rather, Hadoop-like architectures shall be used. Yet, Hadoop does not provide the types and operations necessary for processing such datasets. In this paper, we propose a Hadoop extension (indeed a SpatialHadoop extension) capable of performing analytics on big spatiotemporally referenced event dataset. The extension includes data types and operators that are integrated into the Hadoop core, to be used as natives. We further optimize the querying by means of a spatiotemporal index. Experiments on the gdelt event dataset demonstrate the utility of the proposed extension.

Em 2003, foi criado o Programa de Aquisição de Alimentos (PAA), no qual agricultores familiares comercializam sua produção junto aos órgãos públicos municipais, que a repassam aos restaurantes populares e banco de alimentos. Com o desenvolvimento do PAA, tornou-se necessária a existência de um modelo de avaliação constante do desempenho dos fornecedores, ou seja, dos agricultores familiares. Esta pesquisa teve como objetivo propor um modelo de avaliação dos fornecedores, utilizando um scorecard que avaliasse e classificasse o desempenho dos agricultores familiares do PAA da Secretaria Municipal de Agricultura e Abastecimento (SMAA) do município de São Carlos - SP. A revisão teórica abordou temas referentes à logística e à gestão da cadeia de suprimentos, à medição e avaliação de desempenho e o pedido perfeito. Para o desenvolvimento do estudo de caso, foram realizadas entrevistas com aplicação de questionários semiestruturados junto aos envolvidos no programa. Com base nas informações obtidas, elaborou-se um modelo, que busca conciliar as necessidades da SMAA com os requisitos de um pedido perfeito. O modelo avalia os fornecedores por meio dos indicadores de uniformidade, nível de maturidade, pragas e doenças, pontualidade, entrega completa e entrega futura. Sua aplicação tem o objetivo de promover o desenvolvimento dos fornecedores e contribuir para a melhoria do abastecimento de produtos.

The IASPEI91 global travel-time curves are used as the default for event location at the Prototype International Data Center (PIDC). In order to improve event location, a 1-D Baltic travel-time model was implemented at the PIDC in 1997 for locating events using regional phases from Fennoscandian stations. Where a single model is insufficient for characterizing the regional geology, path-dependent corrections, or Source Specific Station Corrections (SSSCs), are more appropriate for event locations. We have developed SSSCs for regional phases at the Fennoscandian stations by interpolating travel times through different 1-D models. SSSCs for stations NRIS and SPITS are also derived, given the fact that paths from both stations to high latitude events are within the Fennoscandia regionalization as Baltic.Validation testing of the SSSCs demonstrates that using SSSCs in event location is superior to not using SSSCs, and, in most cases, to using the 1-D model directly when locating events. For a ground-truth data set which includes events in the Baltic Shield with location accuracy better than 2 km, the average improvement in location due to SSSCs is 9 km, and the median coverage ellipse is reduced by 2710 km2(from 3830 to 1120 km2). These results are similar to those obtained using the 1-D Baltic model. For a CEB (Calibration Event Bulletin) data set which includes events along the North Atlantic oceanic ridge and in central/southern Europe, using SSSCs the ridge events move closer to the ridge axis, and the European events move closer to CEB locations than 1-D Baltic locations. For a constrained JHD (Joint Hypocenter Determination) data set of events in the Novaya Zemlya region, when using SSSCs or the 1-D Baltic model, relative to the JHD locations mislocations are less or similar to those without SSSCs. All coverage ellipses are smaller but still contain the JHD solutions.Our SSSCs are strongly dependent on the 1-D regional models and regionalization. Future development in 1-D velocity models and travel-time curves should improve such SSSCs, event locations, and uncertainties. It is hoped that the implementation and demonstration of SSSCs in the PIDC software will encourage these further developments. These SSSCs were implemented at the PIDC for Reviewed Event Bulletin (REB) location in April 1999.Key wordsSSSCsFennoscandiatravel timeevent locationIMSPIDC

Gastroenterology: A Field With Endless Career Opportunities for Physicians and Scientists

Performance Evaluation and Measurement for Energy Efficient Wireless Networks

Addressing the policy challenges and opportunities of “Big data”

Recently, graphene family materials (GFMs) have been introduced among all fields of science and still get numerous attention. Also, the applicability of these materials in many areas makes them very attractive. GFMs have attracted both academic and industrial interest as they can produce a dramatic improvement in materials properties at very low filler content. The aim of this review is to identify, summarize, and present the first available information on the influence of GFMs on soil and water environment as well as identify the knowledge gaps and indicate the directions for the next generation of the original scientific investigations. The paper also presents our first preliminary impact assessment and potential pathways of GFMs distribution in the environment. We used as an example the reduced graphene oxide/Al2O3 nanocomposite (RGO/Al2O3) that has been previously designed and synthesized by us. Authors believe that further work should focus on improvement of characterization methodology applicable for ecotoxicity analyses and possible interactions between GFMs and different living ecosystems. Consequently, the potential impact of graphene and its derivatives on environmental health is a matter of academic interest. However, potential hazards sufficient for risk assessment and concerned with GFMs usage in consumer products first need to be investigated and identified. Further research should focus on gathering knowledge on GFMs properties for life cycle analyses, which still poses a great challenge for scientists.

Events are ubiquitous in real-life. With the rapid rise of the popularity of social media channels, massive amounts of event data, such as information about festivals, concerts, or meetings, are increasingly created and shared by users on the Internet. Deriving insights or knowledge from such social media data provides a semantically rich basis for many applications, for instance, social media marketing, service recommendation, sales promotion, or enrichment of existing data sources. In spite of substantial research on discovering valuable knowledge from various types of social media data such as microblog data, check-in data, or GPS trajectories, interestingly there has been only little work on mining event data for useful patterns.
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\nIn this thesis, we focus on the discovery of interesting, useful patterns from datasets of events, where information about these events is shared by and spread across social media platforms. To deal with the existence of heterogeneous event data sources, we propose a comprehensive framework to model events for pattern mining purposes, where each event is described by three components: context, time, and location. This framework allows one to easily define how events are related in terms of conceptual, temporal, and spatial (geographic) relationships. Moreover, we also take into account hierarchies for contexts, time, and locations of events, which naturally exist as useful background knowledge to derive patterns at different levels of abstraction and granularity. Based on this framework, we focus on the following problems: (i) mining interval-based event sequence patterns, (ii) mining periodic event patterns, and (iii) extracting semantic annotations for locations of events. Generally, the first two problems consider correlations of events whereas the last one takes correlations of event components into account.
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\nIn particular, the first problem is a generalization of mining sequential patterns from traditional data, where patterns representing complex temporal relationships among events can be discovered at different levels of abstraction and granularity. The second problem is to find periodic event patterns, where a notion of relaxed periodicity is formulated for events as well as for groups of events that co-occur. The third~problem is to extract semantic annotations for locations on the basis of exploiting correlations of contexts, time, and locations of events. For the three problems above, we respectively propose novel and efficient approaches. Our experiments clearly indicate that extracted patterns and knowledge can be well utilized in various useful tasks, such as event prediction, semantic search for locations, or topic-based clustering of locations.

Silver nanowires (Ag NWs) have attracted much interest in academics and industries because of their potential applications in electronic devices such as flexible displays, pressure sensors, temperature sensors, and conductive layers. Generally, silver nanowire synthesized by the polyol method is the most common and promising chemical reaction. However, its by-products, a mixture of Ag NWs and silver nanoparticles, reduced Ag NWs performance. In this work, we reported the chemical method that is a fast and straightforward synthesis of Ag NWs by using freshly prepared silver chloride to avoid its degradation. The result showed that the freshly prepared silver chloride plays an essential role in promoting the formation of Ag NWs. The by-product was lower compared to using commercial silver chloride. We also found that avoiding boiling bubbles in flask via controlling temperature and reaction time by injecting ethylene glycol, resulting in decreased nanowires degradation. Furthermore, instantaneously decreasing temperature after finishing the cooking time helps reduce its by-product. A high yield of Ag NWs is noticeable under the observed synthetics.

Discovering the knowledge in unstructured archived data is an area of active interest in academia and industry, as it offers opportunities to learn, confirm and eventually, address R&D productivity challenges. Our interest in this area prompted us to investigate an Natural Language Processing based approach to extract unstructured Pharmacokinetics (PK) and Pharmacodynamics (PD) data from PK-PD study reports, and perform analytics using in-house developed compartmental and non-compartmental analytics engines. For this purpose, we have developed a dictionary of two thousand twenty-one (2321) PK-PD keywords based on published study reports. Details of our approach and its applications in discovering the knowledge in the unstructured archived early drug development reports, is the subject of this paper.