Towards Convergence in Information Systems Design

Three technologies—business intelligence, big data, and machine learning—developed independently and address different types of problems. Data warehouses have been used as systems for business intelligence, and NoSQL databases are used for big data. In this chapter, the authors explore the convergence of business intelligence and big data. Traditionally, a data warehouse is implemented on a ROLAP or MOLAP platform. Whereas MOLAP suffers from having propriety architecture, ROLAP suffers from the inherent disadvantages of RDBMS. In order to mitigate the drawbacks of ROLAP, the authors propose implementing a data warehouse on a NoSQL database. They choose Cassandra as their database. For this they start by identifying a generic information model that captures the requirements of the system to-be. They propose mapping rules that map the components of the information model to the Cassandra data model. They finally show a small implementation using an example.

Blockchain Governance for Collaborative Manufacturing

The manufacturing industry is rapidly changing due to widespread adoption of information and communication technologies. This new landscape, described as the fourth industrial revolution, will be characterized by highly complex and interdependent systems. One particular aspect of this shift is horizontal integration, or the tight coupling of firms within a value chain. Highly interconnected and interdependent manufacturing systems will encounter new challenges associated with coordination and collaboration, specifically with regards to trust. This purpose of this chapter is to explore the potential of blockchain to address these challenges. Survey data collected from manufacturing professionals suggests that the perceived nature of trust and resource value can be bounded and controlled. Concepts from game theory, systems theory, and organizational economics are used to augment this research data and inform a collaborative manufacturing blockchain model and architecture.

A Declarative Approach to Systems Development

The declarative approach specifies what is to be done rather than how to do it. When adopted in information systems development, this implies that the system should be seen as a collection of business rules that can be enacted using a business rules engine. Business rules should be expressed in a form that is as close to the one in which business people perceive the rules. A business rules management system is needed to acquire, store, and allow modification of a business rules database. The rules are then handed over to a rules engine for enactment. The BRMS considered in this chapter uses an antecedent-consequent form for representing rules. These are based in a first order logic. Rules are formed with courses of actions and conditions in rules antecedents and courses of actions in rule consequents. It also introduces notions of state change in the business rule and temporal relation within rule and between different rules. Business rules are structured into atomic, complex, and abstract rules. The business rules are translated into enactment rules and converted to Java.

On Ontology of Integration Between Access Control and Business Process Deep Structure

Access control models (ACM) offers the guarantee that only the qualified users can gain access to the artifacts contained in business processes. Business processes are designed, implemented, and operated using many industrial standards that challenge the interoperation with access control standards. Enterprise engineering (EE) introduces rigorous capabilities to design and implement the essential concepts related with the dynamic of business processes. ACM deals with the systematic design and implementation of dynamic and static access control concepts to qualify the access of the users to the artifacts. This chapter proposes an ontological integration between EE and ACM concepts in order to enable the discussion of access control in the deep structure of the business processes. ACM integrated with EE allow the run-time qualification of the actors while they perform all the business process steps and not only at invocation time. The proposal encompasses business process designed with DEMO ontology and role-based access control concepts using a mathematical model logic description.

On Dynamically Reconfiguring IoT Architectures

The world is moving towards a new generation of internet, the internet of things (IoT). This technological jump is improving the way we live by creating a bridge between the physical and the virtual world. Researchers are curious about the field from different perspectives especially managing of the complexity and dynamicity of these systems. The main problem being targeted in this chapter is that IoT systems are exposed to many structural and behavioral changes due to internal and external factors. This study is about the necessity of having a mechanism that enables IoT systems to perform without breaks or shutdowns regardless of context changes. The solution consists of a contextual dynamic reconfiguration process implemented by a reflexive multilayered architecture. This process is based on the autonomic computing loop. The authors also integrated evolution styles to make reusable the reconfigurations applied on the architecture of the system. Validation of the proposed approach was made on an e-health scenario, which was simulated using Cisco Packet Tracer before performing real development.

Industry 4.0-Based Enterprise Information System for Demand-Side Management and Energy Efficiency

The supply demand gap in energy sector in any country is a major challenge. Demand side management (DSM) and energy efficiency (EE) are the well-known solutions in the short term, and capacity addition is the long-term solution. However, both DSM and EE initiatives require significant investment and logistics if implemented in the traditional approach. The contemporary Industry 4.0 principles can be effectively applied to resolve several issues. This chapter proposes a novel enterprise information system (EIS) by treating the modern power systems as cyber physical system and to manage the processes of DSM and EE. A prototype system is suggested to pave the path for EIS, and the functional characteristics are illustrated with a few data visualizations.

Processing Data Streams

A data stream is a real-time continuous sequence that may be comprised of data or events. Data stream processing is different from static data processing which resides in a database. The data stream data is seen only once. It is too voluminous to store statically. A small portion of data called a window is considered at a time for querying, computing aggregates, etc. In this chapter, the authors explain the different types of window movement over incoming data. A query on a stream is repeatedly executed on the new data created by the movement of the window. SQL extensions to handle continuous queries is addressed in this chapter. Streams that contain transactional data as well as those that contain events are considered.

The Evolution of Computational Agency

Agent-based models have emerged as a promising paradigm for addressing ever increasing complexity of information systems. In its initial days in the 1990s when object-oriented modeling was at its peak, an agent was treated as a special kind of “object” that had a persistent state and its own thread of execution. But since then, agent-based models have diversified enormously to even open new conceptual insights about the nature of systems in general. This chapter presents a perspective on the disparate ways in which our understanding of agency, as well as computational models of agency, have evolved. Advances in hardware like GPUs that brought neural networks back to life may also similarly infuse new life into agent-based models and pave the way for significant advancements in research on artificial general intelligence (AGI).

Web Services Reputation Based on Consumer Preferences

With the increasing application of web services in our lives, selecting the right web service is becoming unprecedentedly difficult. Indeed, before paying the price of a web service, the customer always tries to make sure of his choice. One of the mechanisms used to put the customer in trust is to make available the opinions of other customers who have already used this web service. In the literature, many solutions for measuring the reputation of web services have been proposed. Unfortunately, they ignore certain aspects that we find important to ensure a more meaningful assessment of the reputation. Firstly, consumers do not always have the same satisfaction criteria, and as a result, they can judge the same web service differently. Thus, without knowing the consumer's preferences, it is almost impossible to give meaning to his opinion. Secondly, the qualities of a web service can be changed over time, and hence, the old ratings are no longer representative. In this chapter, the authors propose a novel reputation computation approach to deal with the problems mentioned above.

Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)

Optimal selection of materialized views is crucial for enhancing the performance and efficiency of data warehouse to render decisions effectively. Numerous evolutionary optimization algorithms like particle swarm optimization (PSO), genetic algorithm (GA), bee colony optimization (BCO), backtracking search optimization algorithm (BSA), etc. have been used by researchers for the selection of views optimally. Various frameworks like multiple view processing plan (MVPP), lattice, and AND-OR view graphs have been used for representing the problem space of MVS problem. In this chapter, the authors have implemented random walk grey wolf optimizer (RWGWO) algorithm for materialized view selection (i.e., RWGWOMVS) on lattice framework to find an optimal set of views within the space constraint. RWGWOMVS gives superior results in terms of minimum total query processing cost when compared with GA, BSA, and PSO algorithm. The proposed method scales well on increasing the lattice dimensions and on increasing the number of queries triggered by users.