Random function priors for exchangeable arrays with applications to graphs and relational data

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

Computer-based selection of joining processes

The assembly of any manufactured product involves joining. This paper describes ways of selecting processes for joining. The method allows discrimination of the joint geometry, joint loading, material, and other attributes of the joint itself, identifying the subset of available processes capable of meeting a given set of design constraints. A relational database containing data-tables for joining processes, materials to be joined, and joint geometry and mode of loading, allows the attributes of each of these to be stored in an appropriate format, and permits links to be created between those that are related. A search engine isolates the processes that meet design requirements on material, joint geometry and loading. The method is illustrated in Part 2 by case studies, utilising software that embodies the method.

Buyer-supplier relationships in a servitized environment: An examination with Cannon and Perreault's framework

Purpose: The purpose of this study is to examine a buyer's adoption of servitization and the associated implications for the relationships with its suppliers. Design/methodology/approach: The authors use the case study approach to examine the tripartite relationship between a manufacturing company and two of its two suppliers. The paper explores the perspectives of employees on multiple organisational levels, and collects evidence on both sides of a relationship. The authors use template analysis utilising Cannon and Perreault's relationship connectors framework to analyse the data. Findings: There are overarching implications of servitization adoption for buyer-supplier relationships. The implications are notable in all five relationship connectors. Parties expected more open exchange of information, operational linkages were strengthened and changes in the structural arrangements of relationships were witnessed. Legal contracts are complemented by relational norms. The authors also observed a departure away from a win-lose mentality and increased levels of supplier adaptation to support the buyer's provision of integrated solutions. Research limitations/implications: The findings are confined to this tripartite relationship and to an extent are context specific. Practical implications: The study unveils buyer-supplier relationships in a servitized context and provides managers with a better understanding of some of the potential implications that the adoption of a servitization strategy may have for managing buyer-supplier relationships. Originality/value: This is the first empirical study that explores the implications of servitization on buyer-supplier relationships. It advances the understanding of the implications that the adoption of servitization has on the manner in which two parties interrelate and conduct commercial exchange. © Emerald Group Publishing Limited.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Management and Analysis of Unstructured Construction Data Types

Compared with structured data sources that are usually stored and analyzed in spreadsheets, relational databases, and single data tables, unstructured construction data sources such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our vision for data management and mining addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data mining on text-based, web-based, image-based, and network-based construction databases.

Top Management Involvement in New Product Development: A Review and Synthesis

Established literature on new product development (NPD) management recognizes top management involvement (TMI) as one of the most critical success factors. With increasing pressure to sustain competitive advantage and growth, NPD activities remain the focus of close interest from top management in many organizations. TMI in the NPD domain is receiving increasing academic attention. Despite its criticality, there is no systematic review of the existing literature to inform and stimulate researchers in the field for further investigation. This paper introduces the current state of literature on TMI in NPD, synthesizes important findings, and identifies the gaps and deficiencies in this research stream. The contents of the selected articles, which investigated TMI in NPD, are analyzed based on the type of the study, level of analysis, research methodology, operationalization of TMI, and main findings. Additionally, other studies, which did not directly investigate TMI and support in NPD, but were sufficiently related, are briefly summarized. As a result of this detailed literature review, it can be stated that both exploratory and relational studies provide rich evidence on the critical role of top management in NPD. However, the identified gaps and deficiencies in this research stream call for a better theoretical understanding and well-defined constructs of TMI in the NPD domain for different levels of analysis for future studies.

Leadership of healthcare commissioning networks in England: a mixed-methods study on clinical commissioning groups.

To explore the relational challenges for general practitioner (GP) leaders setting up new network-centric commissioning organisations in the recent health policy reform in England, we use innovation network theory to identify key network leadership practices that facilitate healthcare innovation.