A business process re-engineering value in improving the business process is undoubted. Nevertheless, it is incredibly complex, time-consuming and costly. This study aims to review available literature in the use of machine learning for business process re-engineering. The review investigates available literature in business process re-engineering frameworks, methodologies, tools, techniques, and machine-learning applications in automating business process re-engineering. The study covers 200+ research papers published between 2015 and 2020 in reputable scientific publication platforms: Scopus, Emerald, Science Direct, IEEE, and British Library. The results indicate that business process re-engineering is a well-established field with scientifically solid frameworks, methodologies, tools, and techniques, which support decision making by generating and analysing relevant data. The study indicates a wealth of data generated, analysed and utilised throughout business process re-engineering projects, thus making it a potential greenfield for innovative machine-learning applications aiming to reduce implementation costs and manage complexity by exploiting the data’s hiding patterns. This suggests that there were attempts towards applying machine learning in business process management and improvement in general. They address process discovery, process behaviour prediction, process improvement, and process optimisation. The review suggests that expanding the applications to business process re-engineering is promising. The study proposed a machine-learning model for automating business process re-engineering, inspired by the Lean Six Sigma principles of eliminating waste and variance in the business process.

The increased number of Enterprise Social Networks (ESN) business applications has had a major impact on organizations' business processes improvements by allowing the involvement of human interactions to these process. However, these applications generate unstructured data which create barriers and challenges to offering the data in the form of web services in a SOA environment, which again impacts negatively the business process. In this context, the authors propose a framework to interface ESN unstructured data into BP using text mining techniques. The Term frequency-inverse document frequency is used as a weighting schema in this framework. After that, the cosine similarity and k-mean are utilized to find similar values from different documents and cluster documents into groups respectively. The result of the evaluation of the framework shows promising results for retrieving social unstructured data. These results can be published into the SOA enterprise service bus using the RESTful web services.

Using functions in various forms, recent database publications have assigned "scores", "preference values", and "probabilistic values" to object-relational database tuples. We generalize these functions and their evaluations as sideway functions and sideway values, respectively. Sideway values represent the advices (recommendations) of data creators or preferences of users, and are employed for the purposes of ranking query outputs and limiting output sizes during query evaluation as well as for application-dependent querying.This paper introduces SQL extensions and a sideway value algebra (SVA) for object-relational databases. SVA operators modify and propagate sideway values of base relations in automated and generic ways. We define the SVA join, and a recursive SVA closure operator, called TClosure. Output tuples of the SVA join operator are assigned sideway values on the basis of the sideway values and similarities of joined tuples, and the operator returns the highest ranking tuples. TClosure operator recursively expands a given set of objects (as tuples) according to a given regular expression of relationship types, and derives sideway values for the set of newly reached objects.We present evaluation algorithms for SVA join and TClosure operators, and report experimental results on the performance of the operators using the DBLP Bibliography data and synthetic data.

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The aim of this study is to determine the Caracosh (Al-Hamdannia district) groundwater water quality index (CCME WQI). This was calculated by taking groundwater samples and putting them into a thorough physicochemical examination. For calculating the WQI, the following 9 parameters have been considered: pH, TDS, T. Alkalinity, T. hardness, Ca, Mg, Chloride, Sulphate and Nitrate. The WQI for these samples ranges from 25.19 to 93.58. The low values of WQI has been found to be mainly from the higher values of TDS, Total hardness, T. Alkalinity and Sulphate in the groundwater. The analysis reveals that % 40 of groundwater samples of the area needs degree of treatment before consumption, and it also needs to be protected from the perils of contamination.