Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time

We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales

Perceived Diversity of Complex Environmental Systems: Multidimensional Measurement and Synthetic Indicators

The general attitude towards the sustainable management of environmental resources is evolving towards the implementation of ‘participatory’ (as opposed to the classical ‘command and control’) and, especially at local scale, ‘bottom up’ (as opposed to the classical ‘top down’) approaches. This progress pushes a major interest in the development and application of methodologies able to ‘discover’ and ‘measure’ how environmental systems tend to be perceived by the different Stakeholders. Due to the ‘nature’ of the investigated systems, often too ‘complex’ to be treated through a classical deterministic approach, as typical for ‘hard’ physical/mathematical sciences, any ‘measurement’ has necessarily to be multidimensional. In the present report an approach, more typical of ‘soft’ social sciences, is presented and applied to the analysis of the sustainable management of water resources in seven Southern and Eastern Mediterranean Watersheds. The methodology is based on the development and analysis (explorative factor analysis, multidimensional scaling) of a questionnaire and is aimed at the ‘discovery’ and ‘measurement’ of a latent multidimensional ‘underlying structure’ (‘conceptual map’). It is the opinion of the authors, that the identification of a set of ‘consistent’, ‘independent’, ‘bottom up’ and ‘shared’ synthetic indicators (aggregated indices) could be strongly facilitated by the interpretation of the dimensions of the emerging ‘underlying structure’.Participative Approach, Cognitive Map, Factor Analysis, Indicators of Sustainability, Sustainable Water, Management

Medians and Beyond: New Aggregation Techniques for Sensor Networks

Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets

A Report on Mexican Multidimensional Poverty Measurement

This report addresses the challenges arising from a change in Mexico’s official poverty methodology from an income-only basis to a multidimensional basis that includes education, access to health services, access to social security, shelter characteristics, access to basic services, access to food, and level of social cohesion. The concept of poverty underlying this report is drawn from Amartya Sen’s capability approach. The specific multidimensional measurement framework used is that of Alkire and Foster (2007). Special emphasis is placed on the measure’s population decomposability and dimensional decomposability. The new identification and aggregation methods are then applied to 2005 data provided by CONEVAL to illustrate the feasibility of the methodology and the kinds of results that one might obtain.

An Investigation of the Efficacy of Curcumin for Treatment of Alzheimer\u27s Disease

Curcumin is the primary curcuminoid found in the rhizome of the turmeric plant (Curcuma longa), responsible for the spice’s distinctive yellow color. Research conducted within the past two decades suggests that the compound may be an effective treatment for Alzheimer’s disease, the most prevalent form of dementia affecting nearly 5.2 million Americans. This paper investigates the efficacy of curcumin as treatment for the pathogenesis and symptoms of Alzheimer’s. Research was conducted pertaining to the pathogenesis of Alzheimer’s, the in vitro applications of curcumin, the chemical properties of curcumin, and the in vivo clinical applications of curcumin. The pathogenesis of Alzheimer’s is defined by the aggregation of amyloid-beta plaques, dissociation of tau protein, propagation of reactive oxygen species, and neuroinflammation. Alzheimer’s is also characterized by symptoms of cognitive decline and memory loss. The physiochemical nature of curcumin enables it to interact with multiple biochemical pathways in the central nervous system (CNS), inhibiting the pathogenesis of the disease. In vitro applications of curcumin show much promise to this end. In vivo studies of curcumin on living subjects provide mixed results for the substance’s efficacy on symptoms and pathogenesis. Furthermore, the complex chemical properties of curcumin make drug development very difficult. Curcumin shows much promise in inhibiting the pathogenesis of Alzheimer’s, according to in vitro studies. However, the lack of definitive conclusions from in vivo applications and difficulty in overcoming curcumin’s complex chemical properties for drug development show that the substance cannot yet be designated as an effective treatment for the disease

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

Income distribution, standard of living and capabilities: a cross-sectoral analysis.

The aim of the paper is to investigate how agricultural relative incomes have changed in recent years, since the CAP has switched its emphasis from price support to rural development. The distributional implications of agricultural and rural policies are indirectly evaluated looking at the dynamics of earnings and wages in agriculture, as well as at the rural household incomes described through monetary and non monetary variables, so to proxy their living standards. Our concern is not particularly on the agricultural policy tools, as much as on the evaluation of their end results. A comparison spanning through time and across countries is performed on the basis of the information provided by the ECHP and EU-SILC surveys. The paper seeks to unravel the differences between rural and urban population in the different European areas and offers a description of how successes and failures varied, keeping the CAP in the background.Income distribution, Standard of living Earnings in agriculture., Agricultural and Food Policy, D31, E24, J31, N50,