Uncovering the impact of organisational culture types on the willingness to share knowledge between projects

Current literature has established that organisational culture influences knowledge management efforts; however, it is only recently that research on project management has focused its interest on organisational culture in the context of knowledge sharing and some preliminary studies have been conducted. In response, this paper adds a significant contribution by providing rich empirical evidence of the relationships between culture and the willingness to share knowledge, demonstrating which cultural values are more and which are less likely to improve inter-project knowledge sharing behaviours. The use of interviews and the Organisational Culture Assessment Instrument (OCAI) (Cameron & Quinn, 2005) in the cross-case examination of culture in four participating cases has resulted in rich empirical contributions. Furthermore, this paper adds to the project management literature by introducing the Competing Values Framework (CVF) of Cameron and Quinn (2005) to evaluate knowledge sharing in the inter-project context

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin