Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

A data management and analytic model for business intelligence applications

Most organisations use several data management and business intelligence solutions which are on-premise and, or cloud-based to manage and analyse their constantly growing business data. Challenges faced by organisations nowadays include, but are not limited to growth limitations, big data, inadequate analytics, computing, and data storage capabilities. Although these organisations are able to generate reports and dashboards for decision-making in most cases, effective use of their business data and an appropriate business intelligence solution could achieve and retain informed decision-making and allow competitive reaction to the dynamic external environment. A data management and analytic model has been proposed on which organisations could rely for decisive guidance when planning to procure and implement a unified business intelligence solution. To achieve a sound model, literature was reviewed by extensively studying business intelligence in general, and exploring and developing various deployment models and architectures consisting of naïve, on-premise, and cloud-based which revealed their benefits and challenges. The outcome of the literature review was the development of a hybrid business intelligence model and the accompanying architecture as the main contribution to the study.In order to assess the state of business intelligence utilisation, and to validate and improve the proposed architecture, two case studies targeting users and experts were conducted using quantitative and qualitative approaches. The case studies found and established that a decision to procure and implement a successful business intelligence solution is based on a number of crucial elements, such as, applications, devices, tools, business intelligence services, data management and infrastructure. The findings further recognised that the proposed hybrid architecture is the solution for managing complex organisations with serious data challenges.ComputingM. Sc. (Computing

Real-time big data processing for anomaly detection : a survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Lt

Extract, Transform, and Load data from Legacy Systems to Azure Cloud

Internship report presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceIn a world with continuously evolving technologies and hardened competitive markets, organisations need to continually be on guard to grasp cutting edge technology and tools that will help them to surpass any competition that arises. Modern data platforms that incorporate cloud technologies, support organisations to strive and get ahead of their competitors by providing solutions that help them capture and optimally use untapped data, and scalable storages to adapt to ever-growing data quantities. Also, adopt data processing and visualisation tools that help to improve the decision-making process. With many cloud providers available in the market, from small players to major technology corporations, this offers much flexibility to organisations to choose the best cloud technology that will align with their use cases and overall products and services strategy. This internship came up at the time when one of Accenture’s significant client in the financial industry decided to migrate from legacy systems to a cloud-based data infrastructure that is Microsoft Azure cloud. During this internship, development of the data lake, which is a core part of the MDP, was done to understand better the type of challenges that can be faced when migrating data from on-premise legacy systems to a cloud-based infrastructure. Also, provided in this work, are the main recommendations and guidelines when it comes to performing a large scale data migration

The role of technology in improving the Customer Experience in the banking sector: a systematic mapping study

Information Technology (IT) has revolutionized the way we manage our money. The adoption of innovative technologies in banking scenarios allows to access old and new financial services but in a faster and more secure, comfortable, rewarding and engaging way. The number, the performances and the seamless integration of these innovations is a driver for banks to retain their customers and avoid costly change of hearts. The literature is rich in works reporting on the use of technology with direct or indirect impact on the experience of banking customers. Some mapping studies about the adoption of technologies in the field exist, but they are specific to particular technologies (e.g., only Artificial Intelligence), or vice versa too generic (e.g., reviewing the adoption of technologies to support any kind of banking process). So a specific research effort on the crossed domain of technology and Customer Experience (CX) is missing. This paper aims to overcome the following gaps: the lack of a comprehensive map of the research made in the field in the past decade; a discussion on the current research trends of top publications and journals is missing; the next research challenges are yet to be identified. To face these limitations, we designed and submitted 7 different queries to pull papers out of 4 popular scientific databases. From an initial set of 6,756 results, we identified a set of 89 primary studies that we thoroughly analyzed. A selection of the top 20% works allowed us to seek the most performant technologies as well as other promising ones that have not been experimented yet in the field. Main results prove that the combined study of technology and CX in the banking sector is not approached systematically and thus the development of a new specific research line is needed