A brief history of models and model based systems engineering and the case for relational orientation

Models are at the heart of science and engineering. Model-based approaches to software development and systems engineering use technologies to include graphical modeling languages, such as the Systems Modeling Language, that support system design and analysis through machine readable models. This paper traces key historical contributions of software and systems engineers over the past five decades to show a coherent concept of models and how they can be used for software and systems engineering. Recent model-based systems engineering methodologies supported by commercially available modeling tools are also summarized. Relational orientation is seen to be the underlying viewpoint that expresses and binds these approaches. Relational orientation for systems engineering (ROSE) is then specified using a general systems methodology. Systems are seen to access each other's models in ROSE much like classes in object orientation access each other's objects. Object-oriented frames for software engineering are extended to relational frames to specify an innovative framework for system design and analysis. This generalizes the axiomatic design approach of N. P. Suh. A repeatable procedure supporting greater concurrency between design and verification is also demonstrated for searching the solution space in linear axiomatic design

Control design for hybrid systems with TuLiP: The Temporal Logic Planning toolbox

This tutorial describes TuLiP, the Temporal Logic Planning toolbox, a collection of tools for designing controllers for hybrid systems from specifications in temporal logic. The tools support a workflow that starts from a description of desired behavior, and of the system to be controlled. The system can have discrete state, or be a hybrid dynamical system with a mixed discrete and continuous state space. The desired behavior can be represented with temporal logic and discrete transition systems. The system description can include uncontrollable variables that take discrete or continuous values, and represent disturbances and other environmental factors that affect the dynamics, as well as communication signals that affect controller decisions

Weighted Context-Free-Language Ordered Binary Decision Diagrams

Over the years, many variants of Binary Decision Diagrams (BDDs) have been developed to address the deficiencies of vanilla BDDs. A recent innovation is the Context-Free-Language Ordered BDD (CFLOBDD), a hierarchically structured decision diagram, akin to BDDs enhanced with a procedure-call mechanism, which allows substructures to be shared in ways not possible with BDDs. For some functions, CFLOBDDs are exponentially more succinct than BDDs. Unfortunately, the multi-terminal extension of CFLOBDDs, like multi-terminal BDDs, cannot efficiently represent functions of type B^n -> D, when the function's range has many different values. This paper addresses this limitation through a new data structure called Weighted CFLOBDDs (WCFLOBDDs). WCFLOBDDs extend CFLOBDDs using insights from the design of Weighted BDDs (WBDDs) -- BDD-like structures with weights on edges. We show that WCFLOBDDs can be exponentially more succinct than both WBDDs and CFLOBDDs. We also evaluate WCFLOBDDs for quantum-circuit simulation, and find that they perform better than WBDDs and CFLOBDDs on most benchmarks. With a 15-minute timeout, the number of qubits that can be handled by WCFLOBDDs is 1,048,576 for GHZ (1x over CFLOBDDs, 256x over WBDDs); 262,144 for BV and DJ (2x over CFLOBDDs, 64x over WBDDs); and 2,048 for QFT (128x over CFLOBDDs, 2x over WBDDs).Comment: 21 page

Application of Decision Diagrams for Information Storage and Retrieval

Technology is improving at an amazing pace and one reason for this advancement is because of unprecedented growth in the field of Information Technology and also in Digital Integrated Circuit technology over the past few decades. The size of a typical modern database is in the order of high ends of gigabytes and even terabytes. Researchers were successful in designing complex databases but there is still lot of activity on effectively making use of this stored information. There have been significant advancements in the field of Logic optimization and also in Information storage and retrieval but there has been very little transfer of these methods. The purpose of this study is to investigate the use of powerful Computer Aided Design (CAD) techniques for efficient information storage and retrieval. In the work presented in this thesis, it is shown that Decision Diagrams can be used for efficient data storage and information retrieval. An efficient technique is proposed for each of the two key areas of research in Database systems known as Query Optimization and Datamining . Encouraging results are obtained indicating that using hardware techniques for information processing can be a new approach for solving these problems. An SQL query is represented using a hardware data structure known as an AND/OR graph and an SQL parser is interfaced with AND/OR package to achieve query optimization. Optimization using AND/OR graphs works only in the Boolean domain and to make the process of query optimization more complete it has to be investigated in Multivalued domain. The possibility of using MDD as a data structure to represent the query in the multi valued domain is discussed and a synthesis technique is developed to synthesize Multi Valued Logic Networks using MDD. Another useful data structure known as BDD can be used to store the large transaction files used in datamining applications very effectively

GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases

BACKGROUND: Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are performed. A common task is the search of one substructure within one graph, called target. The problem is referred to as one-to-one subgraph search, and it is known to be NP-complete. Heuristics and indexing techniques can be applied to facilitate the search. Indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. The expensive verification phase is then performed only on the subset of promising targets. Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step. Features are memorized in data structures allowing an efficient access. Indexing size, querying time and filtering power are key points for the development of efficient subgraph searching solutions.RESULTS: An existing approach, GRAPES, has been shown to have good performance in terms of speed-up for both one-to-one and one-to-many cases. However, it suffers in the size of the built index. For this reason, we propose GRAPES-DD, a modified version of GRAPES in which the indexing structure has been replaced with a Decision Diagram. Decision Diagrams are a broad class of data structures widely used to encode and manipulate functions efficiently. Experiments on biomedical structures and synthetic graphs have confirmed our expectation showing that GRAPES-DD has substantially reduced the memory utilization compared to GRAPES without worsening the searching time.CONCLUSION: The use of Decision Diagrams for searching in biochemical and biological graphs is completely new and potentially promising thanks to their ability to encode compactly sets by exploiting their structure and regularity, and to manipulate entire sets of elements at once, instead of exploring each single element explicitly. Search strategies based on Decision Diagram makes the indexing for biochemical graphs, and not only, more affordable allowing us to potentially deal with huge and ever growing collections of biochemical and biological structures