Performance evaluation of WMN-GA for different mutation and crossover rates considering number of covered users parameter

Node placement problems have been long investigated in the optimization field due to numerous applications in location science and classification. Facility location problems are showing their usefulness to communication networks, and more especially from Wireless Mesh Networks (WMNs) field. Recently, such problems are showing their usefulness to communication networks, where facilities could be servers or routers offering connectivity services to clients. In this paper, we deal with the effect of mutation and crossover operators in GA for node placement problem. We evaluate the performance of the proposed system using different selection operators and different distributions of router nodes considering number of covered users parameter. The simulation results show that for Linear and Exponential ranking methods, the system has a good performance for all rates of crossover and mutation.Peer ReviewedPostprint (published version

QoS routing in ad-hoc networks using GA and multi-objective optimization

Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version

Searching for the optimal EMU fiscal rule:an ex-post analysis of the SGP reform proposals

Over last decades the study of fiscal policy rules has attracted a growing attention from researchers and policy-makers. The case of European Monetary Union is a clear example. However, even before its inception, the Stability and Growth Pact has been a source of inspiration for a large number of policy recommendations. The heated political and academic debate intervened after the Ecofin Council's decision on November 2003 and mostly concluded in March 2005 with the Spring European Council's conclusions has revealed the institutional and theoretical weaknesses of EMU rule-based system. This paper provides an ex-post analysis of the Pact by indicating a different qualitative and pragmatic approach to judge the most relevant and known SGP reforms; furthermore, it highlights the direction along which any modification of the Pact would have been successfully implemented and offers useful insights also to test the robustness of the new SGP. After revisiting the main characteristics of a fully effective rule-based framework and taking into account the specificity of EMU economic policy set up, we evaluate in a systematic way, through a multivariate statistical analysis, about 100 proposals for reforming the SGP presented by professional academic and non-academic economists prior to April 2005. Despite these large number of proposals, however, principal component analysis outcomes show that only few reforms could have been effectively considered a real improvement of the previous version of SGP, the others reflecting the traditional dilemma between credibility and effectiveness aspects of budgetary rules.Fiscal rules, Fiscal policy, Stability and Growth Pact, European Union Monetary, Principal Component Analysis

TimeMachine: Timeline Generation for Knowledge-Base Entities

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.Comment: To appear at ACM SIGKDD KDD'15. 12pp, 7 fig. With appendix. Demo and other info available at http://cs.stanford.edu/~althoff/timemachine

Integrating OLAP and Ranking: The Ranking-Cube Methodology

Recent years have witnessed an enormous growth of data in business, industry, and Web applications. Database search often returns a large collection of results, which poses challenges to both efficient query processing and effective digest of the query results. To address this problem, ranked search has been introduced to database systems. We study the problem of On-Line Analytical Processing (OLAP) of ranked queries, where ranked queries are conducted in the arbitrary subset of data defined by multi-dimensional selections. While pre-computation and multi-dimensional aggregation is the standard solution for OLAP, materializing dynamic ranking results is unrealistic because the ranking criteria are not known until the query time. To overcome such difficulty, we develop a new ranking cube method that performs semi on-line materialization and semi online computation in this thesis. Its complete life cycle, including cube construction, incremental maintenance, and query processing, is also discussed. We further extend the ranking cube in three dimensions. First, how to answer queries in high-dimensional data. Second, how to answer queries which involves joins over multiple relations. Third, how to answer general preference queries (besides ranked queries, such as skyline queries). Our performance studies show that ranking-cube is orders of magnitude faster than previous approaches

The Geography of Retail Inventory

How different are retailers' inventory levels around the world? Specifically, are retailers' inventories constant across countries, converging, or at least co-integrating? These might be viewed as various forms of global determinism. To see which of these forms hold, I use a novel dataset integrated from Dow Jones, Edgar, Bureau van Dijk (Europe), World'Vest Base, Multex, KIS (Korea Information Service), Teikoku of Japan, Huaxia of China, and COMPUSTAT. The dataset consists of 27,000 firm-year observations for 4,100 retailers in 23 countries, for the period 1983 through 2004. I find evidence to reject all the three forms of global determinism. Instead, I report evidence consistent with an alternative hypothesis - local contingency - in which country effects can explain inventory differences around the world. I also show that this conclusion is robust in numerous ways.Inventory; retailing; international comparison; global determinism; local contingency

Efficient Optimization and Processing of Queries over Text-rich Graph-structured Data

Many databases today capture both, structured and unstructured data. Making use of such hybrid data has become an important topic in research and industry. The efficient evaluation of hybrid data queries is the main topic of this thesis. Novel techniques are proposed that improve the whole processing pipeline, from indexes and query optimization to run-time processing. The contributions are evaluated in extensive experiments showing that the proposed techniques improve upon the state of the art