Mark A. J. Song

A Formal Methodology to Specify E-commerce Systems

Electronic commerce is an important application that has evolved significantly recently. It gives companies the possibility of reaching an unprecedented number of clients at very low cost. However, electronic commerce systems are complex and difficult to be correctly designed. Currently, most approaches are ad-hoc, and frequently lead to expensive, unreliable systems that may take a long time to implement. In this work we propose a methodology that uses formal-method techniques, specifically symbolic model checking, to design electronic commerce applications and to automatically verify that these designs satisfy properties such as atomicity, isolation, and consistency. Using the proposed methodology, the designer is able to identify errors early in the design process and correct them before they propagate to later stages. Thus, it is possible to generate more reliable applications, developed faster and at low costs. In order to demonstrate the applicability and feasibility of the technique, we have modeled and verified a virtual store in which multiple buyers compete for product items. The model verified has more than 1023 states and verification has been completed in few minutes. For instance, the verification process pointed out a concurrency control error which allowed the same item to be sold twice.

The formal-CAFE methodology and model checking patterns in the specification of e-commerce systems

Electronic commerce is an important application that has evolved significantly recently. However, electronic commerce systems are complex and difficult to be correctly designed. Guaranteeing the correctness of an e-commerce system is not an easy task due to the great amount of scenarios where errors occur, many of them very subtle. In this work we presents a methodology that uses formal-method techniques, specifically symbolic model checking, to design electronic commerce applications and to automatically verify them. Also, a model checking pattern hierarchy has been developed—it specifies patterns to construct and verify the formal model of e-commerce systems. We consider this research the first step to the development of a framework, which will integrate the methodology, an e-commerce specification language based on business rules, and a model checker.

Using implications from FCA to represent a two mode network data

In a world of ever-growing connectivity, full of connec- tions between people and objects, new multidisciplinary complex network analysis needs to arise. This work presents a solution to analyze an In- ternet Service Provider database using a formal concept analysis element named implications and complex network techniques. Our goal is to an- alyze access to the 25 most visited websites to find access patterns. We selected 9 time intervals in one week. Data were converted to a clarified formal context and the FindImplications algorithm was used to extract implications sets. These sets were cross-checked to look for patterns. The implications were used to explore the complex network substructures. As a result, we found access patterns that guarantee that whenever premise websites are accessed, so are conclusion websites. This result can aid in creating security policies and network configurations to help predict fu- ture accesses. Without this technique relationships between events nodes (websites) of a two mode network could not be identified.

Handling high dimensionality contexts in formal concept analysis via binary decision diagrams

Abstract In recent years, the increasing complexity of real problems has directed the attention of many types of research, especially those handling large datasets. Formal concept analysis (FCA), for instance, is an increasingly growing research field. It is considered an important theory to formalize the representation of knowledge. FCA uses concept lattice theory to hierarchically organize concepts from a formal context consisting of objects, attributes, and their incidences. However, formal contexts with high dimensionality, considering the number of objects, attributes or both, demand powerful computational resources. Several algorithms have been proposed to extract formal concepts. These algorithms have an exponential complexity in the worst case. So, for high dimensionality contexts, the computational cost becomes prohibitive. This problem motivated several studies seeking alternatives to deal with this type of scenery. Unlike other works, that propose novel algorithms to extract formal concepts, we aimed to improve the performance of FCA algorithms by using an alternative structure, Binary Decision Diagrams (BDD), to represent formal contexts, objects and attributes. The work uses BDD to represent information in a canonical and simplified way to reduce the resources needed to manipulate large datasets, typical scenery for big data problems. Our results show that this approach enables the manipulation of higher dimensional contexts in object quantities, which were unfeasible to original algorithms.

Formal Concept Analysis Applied to Professional Social Networks Analysis.

From the recent proliferation of online social networks, a set of specific type of social network is attracting more and more interest from people all around the world. It is professional social networks, where the users’ interest is oriented to business. The behavior analysis of this type of user can generate knowledge about competences that people have been developed in their professional career. In this scenario, and considering the available amount of information in professional social networks, it has been fundamental the adoption of effective computational methods to analyze these networks. The formal concept analysis (FCA) has been a effective technique tosocial network analysis (SNA) , because it allows identify conceptual structures in data sets, through conceptual lattice and implication rules. Particularly, a specific set of implications rules, know as proper implications, can represent the minimum set of conditions to reach a specific goal. In this work, we proposed a FCA-based approach to identify relations among professional competences through proper implications. The experimental results, with professional profiles from LinkedIn and proper implications extracted fromPropImalgorithm, shows the minimum sets of skills that is necessary to reach job positions.

Distributed BMC: A Depth-First Approach to Explore Clause Symmetry

In recent years new and efficient symbolic model checking algorithms have been developed. One technique, bounded model checking or BMC, has been particularly promising. BMC models the system being verified as a boolean formula whose satisfying assignments provide counterexamples for properties verified. BMC unrolls the system in its multiple iterations. Because of this the structure of the formula representing the system is very symmetric, since all iterations are similar in structure. This work explores this symmetry in a distributed algorithm by postponing the unrolling of the formulas until they are used. This minimizes communication among processors since the formulas transmitted are shorter. Moreover, avoiding the unrolling of conflict clauses has a more pronounced effect, because due to the symmetric nature of the formula, a conflict clause for one instant in the execution can be applied to time multiple instants. As a consequence, short conflict clauses can be unrolled into much more effective clauses, cutting back on the search space significantly. In our experiments we have obtained gains of up to 35% in verification time in some examples.

ImplicPBDD: A New Approach to Extract Proper Implications Set from High-Dimension Formal Contexts Using a Binary Decision Diagram †

Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is increasing substantially. This article presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, called ImplicPBDD, was based on the PropIm algorithm, and uses a data structure called binary decision diagram (BDD) to simplify the representation of the formal context and enhance the extraction of proper implications. In order to analyze the performance of the ImplicPBDD algorithm, we performed tests using synthetic contexts varying the number of objects, attributes and context density. The experiments show that ImplicPBDD has a better performance—up to 80% faster—than its original algorithm, regardless of the number of attributes, objects and densities.

Applying Formal Concept Analysis to assist class hierarchy construction in biomedical systems

The class hierarchy is one of the most important activities of the object-oriented software development. The class design and its hierarchy is a difficult task especially when what is sought is an extensive and complex modeling. An inaccurate or incomplete class hierarchy entails manufacturing defects of the software, making it difficult to maintain or make corrections. The software design in areas like bioinformatics, systems biology and medical informatics usually requires of the designer expertise in biology and medicine, which makes it even more difficult to model class structures. This paper provides guidance for class hierarchy generation which can also be applied to database schema. The use of Formal Concept Analysis provides a theory which enables troubleshoot hierarchy of classes to accomplish the maximum factoring of classes while preserving the relationships of specialization.

A Framework for Photovoltaic and Thermosiphon Systems

Pessimistic forecasts are growing in the Brazilian energy scenario demanding the use of renewable sources of energy such as the solar one. As metropolitan regions have become more populous, private and public companies have developed new technologies based on renewable energy sources. In order to supply such demand, new computer techniques have to be developed. This paper presents a framework to assist the developer to model new components and simulate solar energy applications. By applying the framework concepts, such as source code reuse, one can create a complete environment to evaluate solar energy data. The framework supports software development and tool implementation to be used in photovoltaic and thermosiphon processes.