On the cohomology of the inverse semigroup G of the G-sets of a groupoid G

Renault has defined in [7] the cohomology of the inverse semigroup G of the G-sets of a given groupoid G as a functor from the category of G-presheaves to that of abelian groups. We show in our paper that G-presheaves is isomorphic to AbD(G) where D(G) is the division category defined from Loganathan in [6] and used there to give another description of the Lausch cohomology of inverse semigroups. This isomorphism allows us in turn to prove that Renault and Lausch cohomology groups of G are isomorphic

Job search experiences and career satisfaction among recent archives program graduates

In June 2012, Rebecca Goldman and Shannon Lausch conducted a survey of recent archives graduates to investigate job search experiences, career satisfaction, satisfaction with archival education, and overall life satisfaction. They presented the results of their research at the Society of American Archivists Annual Meeting in San Diego on August 9, 2012. Included here are the survey questions, anonymized versions of the survey data, and Goldman and Lausch\u27s presentations from SAA

Weighted lattice polynomials

We define the concept of weighted lattice polynomial functions as lattice polynomial functions constructed from both variables and parameters. We provide equivalent forms of these functions in an arbitrary bounded distributive lattice. We also show that these functions include the class of discrete Sugeno integrals and that they are characterized by a median based decomposition formula.Comment: Revised version (minor changes

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations