Building Enterprise Transition Plans Through the Development of Collapsing Design Structure Matrices

The United States Air Force (USAF), like many other large enterprises, has evolved over time, expanded its capabilities and has developed focused, yet often redundant, operational silos, functions and information systems (IS). Recent failures in enterprise integration efforts herald a need for a new method that can account for the challenges presented by decades of increases in enterprise complexity, redundancy and Operations and Maintenance (O&M) costs. Product or system-level research has dominated the study of traditional Design Structure Matrices (DSMs) with minimal coverage on enterprise-level issues. This research proposes a new method of collapsing DSMs (C-DSMs) to illustrate and mitigate the problem of enterprise IS redundancy while developing a systems integration plan. Through the use of iterative user constraints and controls, the C-DSM method employs an algorithmic and unbiased approach that automates the creation of a systems integration plan that provides not only a roadmap for complexity reduction, but also cost estimates for milestone evaluation. Inspired by a recent large IS integration program, an example C-DSM of 100 interrelated legacy systems was created. The C-DSM method indicates that if a slow path to integration is selected then cost savings are estimated to surpass integration costs after several iterations

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Complex networks analysis in socioeconomic models

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared for Complexity and Geographical Economics - Topics and Tools, P. Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox-called seqNMF-with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs

Latent Structure in Collaboration: the Case of Reddit r/place

Many Web platforms rely on user collaboration to generate high-quality content: Wiki, Q&A communities, etc. Understanding and modeling the different collaborative behaviors is therefore critical. However, collaboration patterns are difficult to capture when the relationships between users are not directly observable, since they need to be inferred from the user actions. In this work, we propose a solution to this problem by adopting a systemic view of collaboration. Rather than modeling the users as independent actors in the system, we capture their coordinated actions with embedding methods which can, in turn, identify shared objectives and predict future user actions. To validate our approach, we perform a study on a dataset comprising more than 16M user actions, recorded on the online collaborative sandbox Reddit r/place. Participants had access to a drawing canvas where they could change the color of one pixel at every fixed time interval. Users were not grouped in teams nor were given any specific goals, yet they organized themselves into a cohesive social fabric and collaborated to the creation of a multitude of artworks. Our contribution in this paper is multi-fold: i) we perform an in-depth analysis of the Reddit r/place collaborative sandbox, extracting insights about its evolution over time; ii) we propose a predictive method that captures the latent structure of the emergent collaborative efforts; and iii) we show that our method provides an interpretable representation of the social structure

Modular Product Architecture to Manage Supply Chain Complexity

The aim of this article is to demonstrate and to eliminate or to reduce the complexities associated supply chain management. These complexities are aroused due to the high level of interdependencies between component interfaces and supply chain participants. A case example is presented to define the importance of product architecture with the help of design structure matrix (DSM) tool to reduce product development complexity. To address such complexity this work focuses on the product architecture and supply chain design. This modular principle is elaborated with the objective to reduce the supply chain complexities. The relationship between product architecture and supply chain complexities is defined along with identifying and categorizing various drivers responsible for supply chain complexities