David Boaz

Barcelona: A Design and Runtime Environment for Declarative Artifact-Centric BPM

A promising approach to managing business operations is based on business artifacts, a.k.a. business entities with lifecycles [8, 6]. These are key conceptual entities that are central to guiding the operations of a business, and whose content changes as they move through those operations. A business artifact type is modeled using a an information model, which is intended to hold all business-relevant data about entities of this type, and b a lifecycle model, which is intended to hold the possible ways that an entity of this type might progress through the business. In 2010 a declarative style of business artifact lifecycles, called Guard-Stage-Milestone GSM, was introduced [4, 5]. GSM has since been adopted [7] to form the conceptual basis of the OMG Case Management Model and Notation CMMN standard [1]. The Barcelona component of the recently open-sourced [2] ArtiFact system supports both design-time and run-time environments for GSM. Both of these will be illustrated in the proposed demo.

A Generic Business Artifacts Based Authorization Framework for Cross-Enterprise Collaboration

Business artifacts provide an approach to Business Process Management that combines data and process in a holistic way. Previous research introduced artifact-centric Interoperation Hubs (I-Hubs) as a data-centric alternative to conventional service orchestration for enabling the cooperative interaction of multiple organizations with shared business objectives. The current paper extends this vision by describing an approach for implementing I-Hubs that supports rich access control mechanisms. This reflects the data-process duality of business artifacts, the approach borrows from access control disciplines for both data and process. The paper describes how the approach is being applied in connection with two models for artifact lifecycles, a procedural one based on finite state machines and a declarative one based on guards, stages, and milestones.

Self-organizing maps for multi-objective pareto frontiers

Decision makers often need to take into account multiple conflicting objectives when selecting a solution for their problem. This can result in a potentially large number of candidate solutions to be considered. Visualizing a Pareto Frontier, the optimal set of solutions to a multi-objective problem, is considered a difficult task when the problem at hand spans more than three objective functions. We introduce a novel visual-interactive approach to facilitate coping with multi-objective problems. We propose a characterization of the Pareto Frontier data and the tasks decision makers face as they reach their decisions. Following a comprehensive analysis of the design alternatives, we show how a semantically-enhanced Self-Organizing Map, can be utilized to meet the identified tasks. We argue that our newly proposed design provides both consistent orientation of the 2D mapping as well as an appropriate visual representation of individual solutions. We then demonstrate its applicability with two real-world multi-objective case studies. We conclude with a preliminary empirical evaluation and a qualitative usefulness assessment.

Pluggable Analysis Viewpoints for Design Space Exploration

Abstract Viewpoint modeling is an effective approach for analyzing and designing complex systems. Splitting various elements and corresponding constraints into different perspectives of interests, enables separation of concerns such as domains of expertise, levels of abstraction, and stages in lifecycle. Specifically, in Systems Engineering different viewpoints could include functional requirements, physical architecture, safety, geometry, timing, scenarios, etc. Despite partial interdependences, the models are usually developed independently by different parties, using different tools and languages. However, the essence of Systems Engineering requires repetitive integration of many viewpoints in order to find feasible designs and to make good architectural decisions, e.g., in each mapping between consecutive levels of abstraction and in each design space exploration. This integration into one consistent model becomes a significant challenge from both modeling and information management perspectives. In this paper we suggest (1) a unique modular algebraic viewpoint representation robust to design evolution and suitable for generation of the integrated optimization/analysis models, and (2) an underlying ontology-based approach for consistent integration of local viewpoint concepts into the unified design space model. We show an example of an optimization model with different combinations of partially interdependent Analysis Viewpoints. Using the proposed modeling and information management approaches the underlying viewpoints equations can be applied without modification, making the approach pluggable.

Pareto Landscapes Analyses via Graph-Based Modeling for Interactive Decision-Making

We consider two complementary tasks for consuming optimization results of a given multiobjective problem by decision-makers. The underpinning in both exploratory tasks is analyzing Pareto landscapes, and we propose in both cases discrete graph-based reductions. Firstly, we introduce interactive navigation from a given suboptimal reference solution to Pareto efficient solution-points. The proposed traversal mechanism is based upon landscape improvement-transitions from the reference towards Pareto-dominating solutions in a baby-steps fashion – accepting relatively small variations in the design-space. The Efficient Frontier and the archive of Pareto suboptimal points are to be obtained by population-based multiobjective solvers, such as Evolutionary Multiobjective Algorithms. Secondly, we propose a framework for automatically recommending a preferable subset of points belonging to the Frontier that accounts for the decision-maker’s tendencies. We devise a line of action that activates one of two approaches: either recommending the top offensive team – the gain-prone subset of points, or the top defensive team – the loss-averse subset of points. We describe the entire recommendation process and formulate mixed-integer linear programs for solving its combinatorial graph-based problems.

Pareto optimization and tradeoff analysis applied to meta-learning of multiple simulation criteria

Simulation performance may be evaluated according to multiple quality measures that are in competition and their simultaneous consideration poses a conflict. In the current study we propose a practical framework for investigating such simulation performance criteria, exploring the inherent conflicts amongst them and identifying the best available tradeoffs, based upon multiobjective Pareto optimization. This approach necessitates the rigorous derivation of performance criteria to serve as objective functions and undergo vector optimization. We demonstrate the effectiveness of our proposed approach by applying it to a specific Artificial Neural Networks (ANN) simulation, with multiple stochastic quality measures. We formulate performance criteria of this use-case, pose an optimization problem, and solve it by means of a simulation-based Pareto approach. Upon attainment of the underlying Pareto Frontier, we analyze it and prescribe preference-dependent configurations for the optimal simulation training.

Evaluating Multivariate Visualizations as Multi-objective Decision Aids

When choosing a solution, decision makers are often required to account for multiple conflicting objectives. This is a situation that can result in a potentially huge number of candidate solutions. Despite the wide selection of multivariate visualizations that can potentially help decide between various candidates, no designated means exist to assess the effectiveness of these visualizations under different circumstances. As a first contribution in this work, we developed a method to evaluate different types of multivariate visualization. The method focuses on the visualization’s ability to facilitate a better understanding of inter-objective trade-offs as a proxy to more sensible decision making. We used the method to evaluate two existing visualization aids: Parallel-Coordinates and an adaptation of Self Organizing Maps (SOM). Both visualizations were compared with tabular data presentation. Our results show that the first visualization is more effective than a plain tabular visualization for the purpose of multi-objective decision making.