Opportunities For Data Analytics Research Articles

QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS

This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics.
 Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.

Restaurant analytics: Emerging practice and research opportunities

Smart technologies and increased data availability enable restaurateurs to gather more information about customers and their behavior. These data can be combined with data from other sources to make a wide range of strategic and operational restaurant decisions, and can therefore generate tremendous value for restaurants and their customers. This study focuses on discussing the most promising research opportunities in restaurant operations that leverage data analytics. In particular, we focus on specific research questions across restaurant decision domains such as location, reservation and table management, queue management, menu design and engineering, and multichannel order management. For each research question, we motivate its practical and theoretical relevance, identify data sources, propose a methodological approach for analysis, and discuss actionable insights for practitioners. As a result, this paper aims to highlight data analytics opportunities for restaurateurs and inspire researchers to contribute in this domain.

Big data analytics opportunities for applications in process engineering

Abstract Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data.

Collaborative knowledge management to identify data analytics opportunities in additive manufacturing

Collaborative knowledge management to identify data analytics opportunities in additive manufacturing

Application of data analytics for research of digital administrative services in Bulgaria

The article examines issues on the topic of digital administrative services in Bulgaria. In this sense, the paper presents the opportunities for application of data analytics, which are suitable for research of this type of services. In this case, the article uses data from online media content on the researched topic. The main purpose of the article is to apply opportunities for data analytics and to present the relevant analysis on the topic of digital administrative services in Bulgaria. To achieve its goal, the article uses various scientific methods, including study, analysis, research, modeling and experimentation. The results support the main hypothesis of the study, namely defining the benefits of applying data analytics to research the media positioning of digital administrative services in Bulgaria. In conclusion, the paper points out the opportunities for improvement of the research in the subject area with the application of the respective data analysis.

A Flexible IoT Stream Processing Architecture Based on Microservices

The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped into two macro levels: the edge level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core level (consisting of the remote cloud components of the application). The aim of this work is to propose an adaptive microservices architecture for IoT platforms which provides real-time stream processing functionalities that can seamlessly both at the edge-level and cloud-level. More in detail, we introduce the notion of μ-service, a stream processing unit that can be indifferently allocated on the edge and core level, and a Reference Architecture that provides all necessary services (namely Proxy, Adapter and Data Processing μ-services) for dealing with real-time stream processing in a very flexible way. Furthermore, in order to abstract away from the underlying stream processing engine and IoT layers (edge/cloud), we propose: (1) a service definition language consisting of a configuration language based on JSON objects (interoperability), (2) a rule-based query language with basic filter operations that can be compiled to most of the existing stream processing engines (portability), and (3) a combinator language to build pipelines of filter definitions (compositionality). Although our proposal has been designed to extend the Senseioty platform, a proprietary IoT platform developed by FlairBit, it could be adapted to every platform based on similar technologies. As a proof of concept, we provide details of a preliminary prototype based on the Java OSGi framework.

Infer, Predict, and Assure: Accounting Opportunities in Data Analytics

SYNOPSIS The business use of data analytics is growing rapidly in the accounting environment. Similar to many new systems that involve accounting information, data analytics has fundamentally changed task processes, particularly those tasks that provide inference, prediction, and assurance to decision-makers. Thus, accounting researchers and practitioners must consider data analytics and its impact on accounting practice in their work. This paper uses the organizing principles from Mauldin and Ruchala's (1999) meta-theory of accounting information systems (AIS) to identify current data analytics use, examine how data analytics impacts the accounting environment, and discuss challenges and research opportunities.

Cognitive Network Framework for Heterogeneous Wireless Networks

The Internet is used by more than two billion customers around the world and is expected to serve as a global platform for interconnecting cyber-physical objects that form the Internet of Things (IoT). Within the next decade, traffic demands are expected to increase a thousand-fold. This challenge can be addressed by introducing and expanding heterogeneous wireless technologies, which provide higher network capacity, wider coverage and higher quality of service (QoS). However, the heterogeneity and complexity of these networks are a major challenge for traditional control and management systems. Therefore, there is a need for self-manageable and self-configurable networks that support the data produced by the different IoT devices and provide opportunities for data analytics. In this work, a cognitive network framework is proposed, in which the network protocol stack is integrated with a semantic system. The proposed framework provides the bases for building smart networks that observe data from different layers in the network protocol stack and represents it in a hierarchical structure in a knowledge base. The framework employs an ontology that provides an abstraction model for the different heterogeneous wireless devices. The ontology determines the relationships between technology-dependent parameters in the network protocol stack and enables, through the use of inferences, the utilization of the observed data from the network. The use of a cognitive network framework with the network protocol stack allows adding ontologies to describe the data, a solution which could solve the problem of analysing, searching or visualising data.

Data analytics opportunities in a smarter planet

New applications of computing are being enabled by instrumentation of physical entities, aggregation of data, and the analysis of the data. The resulting integration of information and control permits efficient and effective management of complex man-made systems. Examples include transportation systems, buildings, electrical grids, health care systems, governments, and supply chains. Achieving this vision requires extensive data integration and analysis, over diverse, rapidly changing, and often uncertain data. There are many challenges, requiring both new data management techniques as well as new mathematics, forcing new collaborations as the basis of the new "Data Science". Needs and opportunities will be discussed in the context of specific pilots and projects.