The building information modelling trajectory in facilities management: A review

There is a paucity of literature that examines building information modelling (BIM) for asset management within the architecture, engineering, construction and owner-operated (AECO) sector. This paper therefore presents a thorough review of published literature on the latest research and standards development that impact upon BIM and its application in facilities management (FM) during the operations and maintenance (O&M) phase of building usage. The purpose is to generate new ideas and provide polemic clarity geared to intellectually challenge readers from across a range of academic and industrial disciplines. The findings reveal that significant challenges facing the FM sector include the need for: greater consideration of long-term strategic aspirations; amelioration of data integration/interoperability issues; augmented knowledge management; enhanced performance measurement; and enriched training and competence development for facilities managers to better deal with the amorphous range of services covered by FM. Future work is also proposed in several key areas and includes: case studies to observe and report upon current practice and development; and supplementary research related to concepts of knowledge capture in relation to FM and the growing use of BIM for asset management

Generalisation en apprentissage a partir d'exemples

SIGLECNRS T 59806 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Applying Mining with Scoring

“Scoring”, in general, is defined as the usage of mining models - based on historical data - for classification or segmentation of new items. For example: if the historical data consist of classified customers, then we can use the model for the prediction of the behaviour of a new customer. Scoring offers novel ways to exploit the power of data mining models in everyday business activities, and proliferate mining applications to users who are not educated in mining. In this paper, we present a) the generic scoring process b) its technical mplementation, and c) an example of how scoring can be integrated in a real application. The generic process consists of three steps: The mining models are learned first, then they are transferred into the application database, and finally the models are applied to the data loaded in that database. Arguments for the necessity of such a mining improvement are collected. IBM DB2 Intelligent Miner Scoring (IM Scoring) is the first technical implementation of scoring. It is based on the emerging open-standard for mining models (Predictive Model Markup Language - PMML), and the mining extensions for SQL. Implementation issues are discussed, as well as problems that come along with its integration into operational applications. The article closes with the description of a sample application, the integration of scoring into a call center environment. A discussion of the scoring method concludes this article

A Model Elimination Calculus for Generalized Clauses

Generalized clauses differ from (ordinary) clauses by allowing conjunctions of literals in the role of (ordinary) literals, i.e. they are dis junctions of conjunctions of simple literals. An advantage of this clausal form is that implica tions with conjunctive conclusions or disjunc tive premises are not split into multiple clauses. An extension of Lovelands model elimination calculus [Loveland, 1969a, Loveland, 1978] is presented able to deal with such generalized clauses. Furthermore we describe a method for generating lemmas that correspond to valid in stances of conjunctive conclusions. Using these lemmas it is possible to avoid multiple proofs of the premises of implications with conjunctive conclusions.