Ira Monarch

Shared memory in design: A unifying theme for research and practice

This paper presents a new unifying theme for design theory by emphasizing the importance of context. We arrive at our conclusions by examining and then criticizing the legitimacy of universal methods in design upon which the critical importance of context emerges. The collaborative aspects of design focuses attention on the conception of shared meaning. We introduce and elaborate the concept of shared memory as the embodiment both of context and of shared meaning. Using the concept of shared memory in vertical and horizontal forms, within and between disciplines, respectively, we both account for past observations of design in practice and recommend actions to improve design in the future. We examine several practical implications of the growing importance of shared memory in industrial firms and for design teams. We then consider and recommend specific research programs which will help designers capture and make better use of this critical resource.

Boundary Objects and Prototypesat the Interfaces of Engineering Design

The primary hypothesis of this paper is thatinternal and external changes in design andmanufacturing organizations affect theviability of boundary objects (representations,drawings, models – virtual and physical) andrequire changes in the underlying distributedcognitive models. Internal and external factorsinclude new advances in technologies, insightsinto organizational processes, organizationalrestructuring and change of market focus. Ifthe above hypothesis is true, then there areconsequences for the methodologies of designingcomputational support systems for co-operativeengineering work. We provide evidence bydescribing three empirical studies ofengineering design we have performed in largeorganizations. We investigate how changingtechnologies disrupt the common grounds amonginterfaces and how this opens debate onthe role of boundary objects, especially in theproduct visualization and analysis arena. Wethen argue that changes in market forces andother factors leading to changes inorganizational structures often lead to erosionof common understanding of representations andprototypes, above all at the interfaces. Weconclude by making the case that everystructural and information flow change inengineering organizations is accompanied by thepotential deterioration of the common ground.This requires the synthesis of new commongrounds to accommodate the needs of newinterfaces.

Equations aren’t enough: informal modeling in design

Arguing that design is a social process, we expand the meaning of modeling and analysis to include all activities facilitating continual refinement and criticism of the design requirements, process, and solutions. We do not assume any a priori methods for modeling or analysis; rather, we provide a framework and an approach to study designers and give them whatever modeling and analysis capabilities they choose. Our approach is the basis for a support tool, /i-dim, currently under development. 1 The Objective of Modeling and Analysis Design as a social process involving designers, customers, and other participants consists of creating and refining a shared meaning of requirements and potential solutions through continual negotiations, discussions, clarifications, and evaluations. This shared meaning, crystalized as the design artifact and made persistent as shared memory forms the basis of accumulated experience upon which subsequent designs draw. Therefore, design requires support for the following activities: negotiating to establish shared meaning, maintaining and refining the components of the shared meaning, and maintaining and accessing prior information constituting fragments of shared memory. All these requirements are facilitated through iterative modeling and analysis (MA) activities of various forms. If the information about these MA activities is maintained property, the development of shared meanings can be incremental. Therefore, MA activities can rely on previous experience, instead of being rc-invcnied each time, and pitfalls typically encountered in MA can be avoided. In the process of reaching this shared meaning, both modeling and analysis take place, albeit often in an informal and inchoate fashion. For instance, when two designers interact, their exchange involves a particular aspect of the design that is modeled in their discussion. A question posed by one designer constitutes modeling and the response an analysis. Often, the focus of the discussion or negotiation drifts marking the use of several models which, while possibly loosely connected, are nevertheless invaluable for the negotiation. Therefore, to benefit from past models arising in collaborative processes, the information derived from previous negotiations between designers needs to be maintained. Access to information from previous, analogically related, design situations is a basic requirement for improving design. In fact, the very act of accessing and applying previous information implies a model of past information and requires models and analyses of the present. To illustrate, if designers create a quexy to retrieve pans from a database for satisfying a specific function, they model the functionality required using a relatively small set of parameters related to, and perhaps derived from, past models. If the query retrieves useful pans, the analysis was successful and the modeling appropriate. If the query fails, knowledge about the failure constitutes valuable information as well. Consequently; it is necessary that not only successes but also that failures be MA activities manifest in negotiation and information retrieval are by and large informal, as opposed to formal modeling via models cast in mathematical form as traditionally conceived of in engineering.

New roles for machine learning in design

Abstract Research on machine learning in design has concentrated on the use and development of techniques that can solve simple well-defined problems. Invariably, this effort, while important at the early stages of the development of the field, cannot scale up to address real design problems since all existing techniques are based on simplifying assumptions that do not hold for real design. In particular, they do not address the dependence on context and multiple, often conflicting, interests that are constitutive of design. This paper analyzes the present situation and criticises a number of prevailing views. Subsequently, the paper offers an alternative approach whose goal is to advance the use of machine learning in design practice. The approach is partially integrated into a modeling system called n-dim. The use of machine learning in n-dim is presented and open research issues are outlined.

Mapping vocabularies using "latent semantics"

Individual users of medical language manifest great variation in the expression of concepts and have difficulty in selecting appropriate terminology when confronted with systems that rely on standardized language, such as MeSH, SNOMED, or ICD, and the special terms sets of systems such as HELP, INTERNIST-I/QMR, and DXplain. Indeed, the need to map natural language into appropriate special terms—as well as the need to map one system’s specialized terminology into another’s—is one of the problems being addressed by the National Library of Medicine’s UMLS System, with its associated information sources maps. The problem is extremely difficult, in part, because such mappings depend on semantic equivalences among terms, not merely the superficial matching of words or phrases.

Support system for different-time different-place collaboration for concurrent engineering

To be effective in practice, concurrent engineering requires access to and organization of knowledge accumulated over time, product versions, and customers. More important, separate knowledge resources have to be shared and coordinated over space and time if successful design is to be accomplished. The authors, address the nature of communication in design, especially across disciplines, and the support systems that facilitate better communication. While a lot of research effort is being expended on same-time communications within a group, they consider, as well, the need for, and the problems associated with, different-time, different-placed communication. they present these views in connection with an on-going development effort, n-dim.<<ETX>>

Predicting and fixing vulnerabilities before they occur: a big data approach

The number and variety of cyber-attacks is rapidly increasing, and the rate of new software vulnerabilities is also rising dramatically. The cybersecurity community typically reacts to attacks after they occur. Being reactive is costly and can be fatal, where attacks threaten lives, important data, or mission success. Taking a proactive approach, we are: (I) identifying potential attacks before they come to fruition, and based on this identification; (II) developing preventive counter-measures. We describe a Proactive Cybersecurity System (PCS), a layered, modular service platform that applies big data collection and processing tools a wide variety of unstructured data sources to identify potential attacks and develop countermeasures. The PCS provides security analysts a holistic, proactive, and systematic approach to cybersecurity. Here we describe our research vision and progress towards that vision.

Can Cybersecurity Be Proactive? A Big Data Approach and Challenges

The cybersecurity community typically reacts to attacks after they occur. Being reactive is costly and can be fatal where attacks threaten lives, important data, or mission success. But can cybersecurity be done proactively? Our research capitalizes on the Germination Period—the time lag between hacker communities discussing software flaw types and flaws actually being exploited—where proactive measures can be taken. We argue for a novel proactive approach, utilizing big data, for (I) identifying potential attacks before they come to fruition; and based on this identification, (II) developing preventive countermeasures. The big data approach resulted in our vision of the Proactive Cybersecurity System (PCS), a layered, modular service platform that applies big data collection and processing tools to a wide variety of unstructured data sources to predict vulnerabilities and develop countermeasures. Our exploratory study is the first to show the promise of this novel proactive approach and illuminates challenges that need to be addressed.

The Capabilities Engineering Framework: Holistic Approach to Systems of Systems Life Cycle

[Abstract] This report specifies the principles and high-level concepts used to differentiate Capabilities Engineering (CE) from other types of engineering by combining elements of two different perspectives corresponding to the demand (operational) and supply (institutional) sides of the Army. A Capability Engineering Framework (CEF) to align and reconcile these perspectives is prototyped on a real-world example – the Warfighter Mission Area Integrated Working Group. Issues and future objectives for CEF adoption are also outlined. CE is concerned with the formulation and development of capabilities into highquality joint solutions across multiple organizations and disciplines meeting warfighter needs with acquisition incisiveness and efficiency. It is committed to being capabilities and qualities driven in a military arms market that is a bilateral monopoly. It is also committed to being integrated with management processes whose aim is for capabilities to be delivered on time, within budget and according to policy. CE takes a holistic approach to developing and qualifying systems across the capabilities life cycle involving combat, material and product developers in mutual interaction. The CEF guides the incorporation of systems and software engineering know-how as five enabling dimensions of CE termed: virtual organization, cross-organizational processes, ontology of quality attributes, capability evaluation and learning organization.