P-LUPOSDATE: Using Precomputed Bloom Filters to Speed Up SPARQL Processing in the Cloud

Increasingly data on the Web is stored in the form of Semantic Web data. Because of today's information overload, it becomes very important to store and query these big datasets in a scalable way and hence in a distributed fashion. Cloud Computing offers such a distributed environment with dynamic reallocation of computing and storing resources based on needs. In this work we introduce a scalable distributed Semantic Web database in the Cloud. In order to reduce the number of (unnecessary) intermediate results early, we apply bloom filters. Instead of computing bloom filters, a time-consuming task during query processing as it has been done traditionally, we precompute the bloom filters as much as possible and store them in the indices besides the data. The experimental results with data sets up to 1 billion triples show that our approach speeds up query processing significantly and sometimes even reduces the processing time to less than half

A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach

Optimization of sales in fashion retail by warehouse integration in multichannels

Issue of study: The thesis studies the optimization of sales in fashion retail by integrating multichannels. The multichannel integration relates to reallocation of articles between brick and mortar store warehouses and online warehouses. Purpose: The purpose of the thesis is to investigate how increased multichannel integration, in terms of reallocating articles between online store warehouses and brick and mortar store warehouses, affect sales for retailers within the fashion industry. The thesis investigates the potential in increasing the sales by increasing the availability online and by decreasing the share of reductions in price, by reallocating articles to the most suitable channel. Method: The research was conducted as an embedded single case study with one subunit studying the effects of increasing availability online and the other subunit studying the effects of decreasing price reductions. Data was gathered from four main sources: archival records, documentation, interviews, and observations. The analysis was performed by estimating sales figures if the reallocations had not been performed and compare it to the sales figures after the reallocations. Conclusions: By reallocating articles to the online warehouse, the sales on the reallocated articles increased on average by 67 percent, and had the potential of contributing to an overall increase of 1.2 percent. The sales potential can be further increased by a larger share of the additional store stock a larger share of comparable articles between the channels. The reallocation with the purpose of reducing price reductions contributed to a decrease of 19 percent of the reallocated articles, and an overall decrease in the total price reductions in the online store by 0.4 percent. Reallocating articles between channels with the purpose of reducing price reductions is considered to be less risky in terms of the likelihood of sending the wrong article, than the reallocation purpose of increasing sales, which can be explained by the impact of the short life cycles of the articles in fashion retail and thus the impact of timing

Active Inferants: An Active Inference Framework for Ant Colony Behavior

In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá

This paper proposes a modal-shift analysis methodology based on a mix of small-scale primary data and big data sources to estimate the total amount of trips that are reallocated to transportation network companies (TNCs) services in Bogotá, Colombia. The analysis is focused on the following four modes: public transportation, private vehicles, conventional taxis, and TNC services. Based on a stated preferences survey and secondary databases of travel times and costs, the paper proposes a methodology to estimate the reallocation of travel demand once TNCs start operating. Results suggests that approximately one third of public transportation trips are potentially transferred to TNCs. Moreover, potential taxi and private vehicle–transferred trips account for almost 30% of the new TNC demand. Additionally, approximately half of the trips that are reallocated from public transport demand can be considered as complementary, while the remaining share can be considered as potential replacing trips of public transportation. The paper also estimates the potential increase in Vehicle-km travelled in each of the modes before and after substitution as a proxy to the effects of demand reallocation on sustainability, finding increases between 1.3 and 14.5 times the number of Vehicle-km depending on the mode. The paper highlights the role of open data and critical perspectives on available information to analyze potential scenarios of the introduction of disruptive technologies and their spatial, social, and economic implications

Study on the feasibility of a tool to measure the macroeconomic impact of structural reforms

The main aim of this study is to assess the feasibility of empirical tools to study the impact of structural reforms on the macroeconomic performance in the member countries of the European Union (EU). This report presents the results of the project "Study on the feasibility of a tool to measure the macroeconomic impact of structural reforms" (ECFIN-E/2005/001) and amalgamates the findings of the two previous interim reports and the main conclusions of the workshop held in Brussels on May 11th. The main goal of the project is to determine the most reliable and robust methods to investigate the impacts of economy-wide structural reforms as well as reforms in individual markets or sectors, and to make suggestions as to how they best to implement them and possible improvements of the institutional dataset. In addition, a roadmap has been created which includes the main steps in the model-developing process, and solutions feasible even in the short term are discussed.The most relevant conclusion to be drawn from the study is that the most appropriate tool that can be developed in the short term is the integration of a DSGE model (preferably QUEST due to its in-house availability) with different satellite models, to be developed.structural reforms, product markets, labour markets, financial markets, Dreger, Art�s, Moreno, Ramos, Suri�ach