18 research outputs found
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Time Dimension for Business News in the Knowledge Warehouse
This paper proposes a data structure to support the integration of business news and data in the knowledge warehouse using the Temporal Document Retrieval Model (TDRM). Temporal document retrieval takes into account the time context expressed as temporal phrases in the document content. The maintenance and scalability of the proposed data cluster is discussed. The results of temporal document retrieval experiments based on typical and ad-hoc time constraints, performed on a large corpus of business news are reported. The experiments showed that the instantaneous TDRM-based retrieval using the proposed cluster is possible with the currently available database technology and confirmed the highly constraining character of the temporal context
The obnoxious facilities planar p-median problem
In this paper we propose the planar obnoxious p-median problem. In the
p-median problem the objective is to find p locations for facilities that
minimize the weighted sum of distances between demand points and their closest
facility. In the obnoxious version we add constraints that each facility must
be located at least a certain distance from a partial set of demand points
because they generate nuisance affecting these demand points. The resulting
problem is extremely non-convex and traditional non-linear solvers such as
SNOPT are not efficient. An efficient solution method based on Voronoi diagrams
is proposed and tested. We also constructed the efficient frontiers of the test
problems to assist the planers in making location decisions
Directional approach to gradual cover: the continuous case
The objective of the cover location models is covering demand by facilities
within a given distance. The gradual (or partial) cover replaces abrupt drop
from full cover to no cover by defining gradual decline in cover. In this paper
we use a recently proposed rule for calculating the joint cover of a demand
point by several facilities termed "directional gradual cover". Contrary to all
gradual cover models, the joint cover depends on the facilities' directions. In
order to calculate the joint cover, existing models apply the partial cover by
each facility disregarding their direction. We develop a genetic algorithm to
solve the facilities location problem and also solve the problem for facilities
that can be located anywhere in the plane. The proposed modifications were
extensively tested on a case study of covering Orange County, California
Dynamic Prediction of retail Website Visitors\u27 Intentions
This paper presents a model for identifying general intentions of consumers visiting a retail website. When visiting a transactional website, consumers have various intentions such as browsing (i.e., no purchase intention), purchasing a product in the near future, or purchasing a particular product during their current visit. By predicting these intentions early in the visit, online merchants could personalize their offer to better fulfill the needs of consumers. We propose a simple model which enables classifying visitors according to their intentions after only four traversals (clicks). The model is based solely on navigation patterns which can be automatically extracted from clickstream. The results are presented and extensions of the model are proposed
The planar multiple obnoxious facilities location problem: A Voronoi based heuristic
Consider the situation where a given number of facilities are to be located in a convex polygon with the objective of maximizing the minimum distance between facilities and a given set of communities with the important additional condition that the facilities have to be farther than a certain distance from one another. This continuous multiple obnoxious facility location problem, which has two variants, is very complex to solve using commercial nonlinear optimizers. We propose a mathematical formulation and a heuristic approach based on Voronoi diagrams and an optimally solved binary linear program. As there are no nonlinear optimization solvers that guarantee optimality, we compare our results with a popular multi-start approach using interior point, genetic algorithm (GA), and sparse non-linear optimizer (SNOPT) solvers in Matlab. These are state of the art solvers for dealing with constrained non linear problems. Each instance is solved using 100 randomly generated starting solutions and the overall best is then selected. It was found that the proposed heuristic results are much better and were obtained in a fraction of the computer time required by the other methods.The multiple obnoxious location problem is a perfect example where all-purpose non-linear non-convex solvers perform poorly and hence the best way forward is to design and analyze heuristics that have the power and the exibility to deal with such a high level of complexity