An Architecture for Personal Cognitive Assistance

Current desktop environments provide weak support for carrying out complex user-oriented tasks. Although individual applications are becoming increasingly sophisticated and featurerich, users must map their high-level goals to the low-level operational vocabulary of applications, and deal with a myriad of routine tasks (such as keeping up with email, keeping calendars and web sites up-to-date, etc.). An alternative vision is that of a personal cognitive assistant. Like a good secretary, such an assistant would help users accomplish their high-level goals, coordinating the use of multiple applications, automatically handling routine tasks, and, most importantly, adapting to the individual needs of a user over time. In this paper we describe the architecture and its implementation for a personal cognitive assistant called RADAR. Key features include (a) extensibility through the use of a plug-in agent architecture (b) transparent integration with legacy applications and data of today’s desktop environments, and (c) extensive use of learning so that the environment adapts to the individual user over time

End User Orchestrations

Service-orchestrations define how services can be composed together and are widely used to execute applications based on Service Oriented Architectures (SOAs). However, the various special purpose orchestration languages used today require code-level constructs that force the users to provide excessive technical detail. For many SOA domains end-users of these orchestrations have limited technical expertise, and hence these users find it difficult to specify orchestrations in current languages. Additionally, users specifying orchestrations would often like to reason about architectural attributes such as performance, security and composability - capabilities that current languages and tools do not support. In this paper we provide an improved technique for modeling orchestrations that allows users to focus primarily on the functional composition of services that is guided by tool supported domain-specific analyses. We introduce an abstract architectural specification language called SCORE (Simple Compositional ORchestration for End users) that defines the vocabulary of elements that can be used in a service composition. SCORE not only allows users to create correct service orchestrations, but it also removes the need for technical detail, most of which is auto-generated by tool support. We demonstrate the use of our approach to specify service-orchestrations in SORASCS (Service ORiented Architectures for Socio-Cultural Systems), which is a SOA system for the intelligence analysis domain. SORASCS users are analysts, who are involved with domain-specific analysis workflows that are represented using SCORE and executed.</p

Using Gauges for Architecture-Based Monitoring and Adaptation

An increasingly important requirement for complex systems is the capability to adapt at runtime in order to accommodate variable resources, system errors, and changing requirements. An essential aspect of adaptation is the ability to observe a system’s runtime behavior and interpret those observations in terms that permit a high-level understanding of the system’s status. In this paper we describe mechanisms for monitoring a running system, interpreting monitored data in terms of the system’s architecture, and analyzing the system architecture to ascertain if the system’s runtime behavior fits within the envelope of acceptable behavior. We illustrate the use of these mechanisms for the important special case of system performance monitoring

Analytic Dependency Loops in Architectural Models of Cyber-Physical Systems

<p>Rigorous engineering of safety-critical Cyber- Physical Systems (CPS) requires integration of heterogeneous modeling methods from different disciplines. It is often necessary to view this integration from the perspective of analyses – algorithms that read and change models. Although analytic integration supports formal contract-based verification of model evolution, it suffers from the limitation of analytic dependency loops. Dependency loops between analyses cannot be resolved based on the existing contract-based verification. This paper makes a step towards using rich architectural description to resolve circular analytic dependencies. We characterize the dependency loop problem and discuss three algorithmic approaches to resolving such loops: analysis iteration, constraint solving, and genetic search. These approaches take advantage of information in multi-view architectures to resolve analytic dependency loops</p

Evolution styles: foundations and models for software architecture evolution

As new market opportunities, technologies, platforms, and frameworks become available, systems require large-scale and systematic architectural restructuring to accommodate them. Today’s architects have few techniques to help them plan this architecture evolution. In particular, they have little assistance in planning alternative evolution paths, trading off various aspects of the different paths, or knowing best practices for particular domains. In this paper, we describe an approach for planning and reasoning about architecture evolution. Our approach focuses on providing architects with the means to model prospective evolution paths and supporting analysis to select among these candidate paths. To demonstrate the usefulness of our approach, we show how it can be applied to an actual architecture evolution. In addition, we present some theoretical results about our evolution path constraint specification language.</p

RAIDE for Engineering Architecture-Based Self-Adaptive Systems

Rainbow is an approach for engineering self-adaptive systems, with run-time, closed-loop control over target systems to monitor, detect, decide, and act on opportunities for system improvement. RAIDE enables adaptation engineers to customize the Rainbow framework, simulate adaptation behavior, and deploy Rainbow run-time components

Impact Models for Architecture-Based Self-Adaptive Systems

<p>Self-adaptive systems have the ability to adapt their behavior to dynamic operation conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is an impact model language equipped with a formal semantics defined in terms of Discrete Time Markov Chains. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decisionmaking in the Rainbow framework for architecture-based self-adaptation.</p

Foundations and Tools for End-User Architecting

<p>Within an increasing number of domains an important emerging need is the ability for technically naïve users to compose computational elements into novel configurations. Examples include astronomers who create new analysis pipelines to process telescopic data, intelligence analysts who must process diverse sources of unstructured text to discover socio-technical trends, and medical researchers who have to process brain image data in new ways to understand disease pathways. Creating such compositions today typically requires low-level technical expertise, limiting the use of computational methods and increasing the cost of using them. In this paper we describe an approach — which we term <em>end-user architecting</em> — that exploits the similarity between such compositional activities and those of software architects. Drawing on the rich heritage of software architecture languages, methods, and tools, we show how those techniques can be adapted to support end users in composing rich computational systems through domain-specific compositional paradigms and component repositories, without requiring that they have knowledge of the low-level implementation details of the components or the compositional infrastructure. Further, we outline a set of open research challenges that the area of end-user architecting raises.</p

Model-based Assistance for Making Time/Fidelity Trade-offs in Component Compositions

<p>In many scientific fields, simulations and analyses require compositions of computational entities such as web-services, programs, and applications. In such fields, users may want various trade-offs between different qualities. Examples include: (i) performing a quick approximation vs. an accurate, but slower, experiment, (ii) using local slower execution environments vs. remote, but advanced, computing facilities, (iii) using quicker approximation algorithms vs. computationally expensive algorithms with smaller data. However, such trade-offs are difficult to make as many such decisions today are either (a) wired into a fixed configuration and cannot be changed, or (b) require detailed systems knowledge and experimentation to determine what configuration to use. In this paper we propose an approach that uses architectural models coupled with automated design space generation for making fidelity and timeliness trade-offs. We illustrate this approach through an example in the intelligence analysis domain.</p

Diagnosing architectural run-time failures

Institute for Software Researc