No abstract

Business process modeling (BPM) is procedural-oriented advanced activity for representing process models with domain knowledge. It consists of all the functional requirements given by the users to build the software. However, nonfunctional requirements such as cost, time, performance, and security are used to evaluate the performance of a system. Among these requirements, security is the vital attribute to be taken care at the early stage of any software development. For example, even governing bodies are heavily dependents on the data system due to security exposures. So automation on model transformation from non-secured metamodel to secured metamodels improves the overall execution of the software evolution process. In this paper, the authors propose an Auto Secure Business Process (AutoSBP) system to automate security to the existing software models. The activity model of existing system is dismantled into attributes and actions to extract its business need.Based on the interpretation from business data, security implications are applied to the existing software models. To fine tune the security implications, ID3 decision learning algorithm is applied. The effect of the system-generated model assures the quality by its complexity metrics.

An area of medical science, that is, gaining prominence, is DNA sequencing. Genetic mutations responsible for the disease have been detected using DNA sequencing. The research is focusing on pattern identification methodologies for dealing with DNA-sequencing problems relating to various applications. A few examples of such problems are alignment and assembly of short reads from next generation sequencing (NGS), comparing DNA sequences, and determining the frequency of a pattern in a sequence. The approximate matching of DNA sequences is also well suited for many applications equivalent to the exact matching of the sequence since the DNA sequences are often subject to mutation. Consequently, recognizing pattern similarity becomes necessary. Furthermore, it can also be used in virtually every application that calls for pattern matching, for example, spell-checking, spam filtering, and search engines. According to the traditional approach, finding a similar pattern in the case where the sequence length is ls and the pattern length is lp occurs in O (ls ∗ lp). This heavy processing is caused by comparing every character of the sequence repeatedly with the pattern. The research intended to reduce the time complexity of the pattern matching by introducing an approach named “optimized pattern similarity identification” (OPSI). This methodology constructs a table, entitled “shift beyond for avoiding redundant comparison” (SBARC), to bypass the characters in the texts that are already compared with the pattern. The table pertains to the information about the character distance to be skipped in the matching. OPSI discovers at most spots of similar patterns occur in the sequence (by ignoring è mismatches). The experiment resulted in the time complexity identified as O (ls. è). In comparison to the size of the pattern, the allowed number of mismatches will be much smaller. Aspects such as scalability, generalizability, and performance of the OPSI algorithm are discussed. In comparison with the hamming distance-based approximate pattern matching algorithm, the proposed algorithm is found to be 69% more efficient.

Many data mining algorithms require the result to be transformed into tabular format. Tabular datasets are the suitable input for many data mining approaches. But the existing SQL aggregations cannot produce results in tabular form with more summarized details especially in horizontal tabular form. Here discuss several approaches to produce data sets in tabular format and also present an efficient method to produce results in horizontal tabular format. Alternative methods for the evaluation of new format are also shown here.

No abstract