In this paper we address the problem of modeling collaborative decision and knowledge intensive business processes (sometimes referred to as Decision Intensive Processes, or DIP processes). DIP processes assist users in performing decision intensive tasks, and provide users with a guidance relevant to process execution context. DIP processes are by nature collaborative, data-driven, need to support various kinds of flexibility at design and run time, and need to integrate with external services and information sources. Such a combination presents significant challenges for contemporary business processes technologies. We present a solution based on a business artifacts paradigm (a.k.a. business entities with lifecycles) using a Guard-Stage-Milestone (GSM) model for declarative lifecycles specification. We introduce a CoreControl - MicroProcess process design pattern, which allows a natural blending of a business functional process structure (usual for most business processes), with a decision & knowledge driven structure providing domain specific decision guidance to users. The proposed design pattern along with the declarative GSM BA approach provide suitable design primitives for DIP process, as demonstrated on a real problem from the supply chain solutions enablement domain.

In a paper by Hawthorn and DeWitt, the projected performance of several proposed database machines was examined for three relational database queries. The present paper investigates the performance of a massively parallel machine called NON-VON for the same queries under comparable assumptions. In the case of simple queries, a NON-VON machine of comparable size to those considered by Hawthorn and DeWitt is found to be somewhat faster than the fastest machines examined in their study; for a more complex database operation, NON-VON is shown to be five to ten times faster than the fastest of these machines.

Distributed algorithms tend to be difficult to understand and even more difficult to prove correct. Using distributed dead-lock detection as a running example this paper presents a framework for stating, understanding, and proving the correctness of distributed algorithms for decision problems. The framework consists of a series of complexity levels. To simplify the initial levels, we treat the data structure of the algorithm as a database, and use the database notions of views and transaction atomicity. For each complexity level, we state theorems that need to be proved for each algorithm. The framework is illustrated using several existing deadlock detection algorithms. Finally, it is shown that the framework suggests new algorithms using the best features of several existing algorithms.

Distributed computing with widely separated machines is a subject of growing theoretical and practical interest. This paper attempts to present a framework for the analysis of message-based distributed computations. This is done in the context of the classical critical section problem and the high-level language, PLITS. The proof techniques described are based on the use of finite-state machines which characterize the external behavior of each module in the distributed computation.