Employee Satisfaction and Corporate Performance in the UK

The volume of data in the world is growing very fast and generated from verity of sources like social media, sensors airline industry or scientific data in different formats. Biggest challenge is how to infer meaningful insights from such a varietyful and big data along with concern of data storage and management of fast growing data. The size of the databases used in today’s enterprises has been growing at exponential rates day by day. Hence, industries requirement to quickly process and analyze the big data volumes for business decision making and customer insights has also grown exponentially. Data pouring from various sources may be can be structured or unstructured in nature. Structured data refers to a relatively well-organized information, which can be further inserted into traditional RDBMS. As Traditional RDBMS are efficient and easy queries by simple, straightforward search algorithms or SQL queries. In contrast to structured data, unstructured data can be considered as information, which does not, comes in a pre-defined data format, well organized data storage model, or cannot be stored well into relational tables. It is assumed to be fastest growing type of data, e.g. image, sensors data, web chats, social networking messaging data, video, documents, log files, and email data. There are many techniques and software available, which can process and provide efficient storage of unstructured data and help organization to perform analytics on unstructured data. Unstructured data does not well-organized and not stored in predefined manner e.g. logs, web chats. The variety and on ordered nature of data makes storage methods and structure makes execution a time and resource-consuming affair. Advancement into technology has open floodgates to push huge volume of unstructured type of data. Multimedia data is one of the example of unstructured big data, which spans all over the Internet. This needs high execution capability to extract useful information. Rapid processing of multimedia data such as video is important for e.g. criminal investigations, surveillance monitoring, news analysis, sports analytics domain, emotion extraction, etc. Hence, analysis of multimedia data in minimum timeframe is one of the latest research areas. Therefore, we have researched techniques for analyzing unstructured data to extract meaningful information hidden in the big data. In addition, we will describe about various techniques and software used to Manage, process unstructured big data in efficient manner, and increases the performance of complexity analysis.

Big data is a valuable asset for organisation as it analyses and help to understand the customers, changes within their business environment, market analysis and future trends. The big data is multifaceted (different data types and versatile), and mostly exists in unstructured formats. The extraction of value from this data is challenging. The usability and productivity of this multifaceted unstructured data is greatly compromised. A number of factors and associated reasons affect the usability of unstructured big data. The present research work investigates these factors and associated reasons behind the usability issues of multifaceted unstructured big data. The identification of these factors contribute to develop solutions to reduce the lack of usability of highly unstructured big data. A detailed study of existing literature followed by survey questionnaire has been conducted to identify the factors and their reasons. Descriptive statistics has been used to analyse and interpret the data and results.

The existing big data platforms take data through distributed processing platforms and store them in a data lake. The architectures such as Lambda and Kappa address the real-time and batch processing of data. Such systems provide real time analytics on the raw data and delayed analytics on the curated data. The data denormalization, creation and maintenance of a columnar dimensional data warehouse is usually time consuming with no or limited support for unstructured data. The system introduced in this paper automatically creates and dynamically maintains its data warehouse as a part of its big data pipeline in addition to its data lake. It creates its data warehouse on structured, semi-structured and unstructured data. It uses Machine Learning to identify and create dimensions. It also establishes relations among data from different data sources and creates the corresponding dimensions. It dynamically optimizes the dimensions based on the crowd sourced data provided by end users and also based on query analysis.

Geospatial data in databases have shifted to conform to the characteristics of big-data in tandem with the development of the Internet, mobile Internet, cloud computing, and especially, spatial data acquisition technologies. Faced with tackling spatial big data, traditional spatial database management techniques based on Relational Database Management Systems have encountered problems including the unstructured characteristics of the spatial object, the high scalability of storage capacity, and the high concurrency in big data application environment. This paper focuses on the mainstream of NoSQL databases that successfully deal with unstructured big data and are widely used in Internet applications, but lack of spatial characteristics. The data operational and query modes cannot meet the requirments of GIS applications. To resolve this problem, this paper proposes a strategy that takes a NoSQL database as a warehouse for spatial big data and a traditional spatial database as the application server. The storage system architecture and the key technology and solutions are discussed. A prototype system was developed based on MongoDB, PostgreSQL and SQLite to verify the feasibility and effectiveness of the strategy.

The amount of data in our world has been exploding. Hence, integrating, managing and analyzing large amounts of data – i.e. big data - will become a key issue for businesses for better operating and competing in today’s markets. We strongly believe that big data are really useful if used in a smart way. So we introduce the concept of Smart Data that is web and enterprise structured and unstructured big data with explicit and implicit semantics that leverages context to understand intent for better driving business processes and for better and more informed decisions making. This paper proposes a language able to give a representation of big data based on ontologies and a system that implements an approach capable to satisfy the increasing need for efficiency and scalability in semantic data management. The proposed MANTRA Language allows for: (i) representing the semantics of data by knowledge representation constructs; (ii) acquiring big data from disparate heterogeneous sources (e.g. data bases, documents), integrating and managing them; (iii) reasoning and querying with big data. The syntax of the proposed language is partially derived from logic programming, but the semantic is completely revised. The novelty of the language we propose is that a class can be thought of as a flexible collection of structurally heterogeneous individuals that have different properties (schema-less). The language also allows executing efficient querying and reasoning for revealing implicit knowledge. These have been achieved by using a triple-based data persistency model and a scalable No-SQL storage system.

The significance of big data is increasingly acknowledged across all sectors, including medicine. Moreover, the trend of data trading is on the rise, particularly in exchanging other data for medical data to rejuvenate the medical industry. This study aimed to discern the facilitating factors of healthcare data trade. We assessed five medical data market platforms on October, 2022, based on three criteria: (a) clarity in articulating the data for sale; (b) transparency in specifying the data costs; and (c) explicit indication that payment grants data access. This helped identify the traded medical data types. Additionally, we anonymously surveyed 43 representatives from medical device companies about their demand for medical data trading, achieving a response rate of 66%. Of the medical data traded on these platforms, 93.34% was structured, while 5.66% was unstructured, indicating an imbalance. Although there was a higher demand for structured medical data, there was also interest in purchasing unstructured medical data. Unstructured big data are crucial for medical device development, fueling the demand for trading such data. Many stakeholders view the data market as essential and are willing to procure medical data. Consequently, medical device companies will need methods to acquire unstructured medical data for developing innovative and enhanced medical devices.

Evaluating medical travelers’ satisfaction through online review analysis

Technological discoveries and the spread of digital technologies require social scientists to revise basic philosophical ideas about the world, society, and man. Issues related to the preservation of freedom, protection of private life and privacy of a person are becoming especially relevant in the digital society. The article considers big data as a tool of social control, supervision and as a means of predicting the behavior of individuals. With the help of big data, large platform companies can shape the core values, cultural codes and consumer behavior of users. The evolution of models of social control is considered: panopticon, synopticon, superpanopticon. While the panopticon is characterized by disciplinary power, the digital society that uses unstructured big data is characterized by instrumental power. The theory of "supervisory capitalism" by S. Zuboff is analyzed and two features of "supervisory capitalism" are distinguished: profit extraction and forecasting of user behavior. In conclusion, having considered the main models of social surveillance, the author highlights the features of social control in a digital society. Social control becomes more subtle, latent, total and indifferent towards a person. The superpanopticon is developing as a social model associated with universal observation based on big data.

Topics such as computational sources and cloud-based transmission and security of big data have turned out to be a major new domain of exploration due to the exponential evolution of cloud-based data and grid facilities. Various categories of cloud services have been utilized more and more widely across a variety of fields like military, army systems, medical databases, and more, in order to manage data storage and resource calculations. Attribute-based encipherment (ABE) is one of the more efficient algorithms that leads to better consignment and safety of information located within such cloud-based storage amenities. Many outmoded ABE practices are useful for smaller datasets to produce fixed-size cryptograms with restricted computational properties, in which their characteristics are measured as evidence and stagnant standards used to generate the key, encipherment, and decipherment means alike. To surmount the existing problems with such limited methods, in this work, a dynamic nonlinear poly randomized quantum hash system is applied to enhance the safety of cloud-based information. In the proposed work, users’ attributes are guaranteed with the help of a dynamic nonlinear poly randomized equation to initialize the chaotic key, encipherment, and decipherment. In this standard, structured and unstructured big data from clinical datasets are utilized as inputs. Real-time simulated outcomes demonstrate that the stated standard has superior exactness, achieving over 90% accuracy with respect to bit change and over 95% accuracy with respect to dynamic key generation, encipherment time, and decipherment time compared to existing models from the field and literature. Experimental results are demonstrated that the proposed cloud security standard has a good efficiency in terms of key generation, encoding, and decoding process than the conventional methods in a cloud computing environment.

The term “Big Data” refers to a large volume of information usually in terabytes and petabytes. It includes both structured and unstructured data. Unstructured data is conventionally text-heavy, but may also contain data such as facts, dates and numbers. To use this unstructured information effectively, it needs to be processed and organized. Taxonomy is considered as a powerful way of organizing the information. For automatic taxonomy generation, various techniques have been proposed in the past. However, the substantial nature of big data presently crosses the processing abilities of traditional techniques. Thus, to meet this challenge an extensible and scalable technique is required to potentially accelerate the process of taxonomy generation and its evolution upon arrival of new data, hence catering large amount of unstructured big data. This paper proposes a technique for both the taxonomy generation and evolution on Apache Spark infrastructure. The proposed technique is evaluated on a text dataset of a computing domain. The evaluation results show that the technique presented in this paper outperformed the existing techniques in terms of time and quality metrics. The time and quality-based evaluation showed that the use of MapReduce environment has resolved the scalability issues of current taxonomy generation and evolution process.

The purpose of this paper is to examine the mediating effect of the rate of quality of accounting information systems on the relationship between big data technology and firms’ financial performance in firms listed on the Palestine Stock Exchange. The researchers conducted an account of the previous studies in this field. The researcher used the deductive approach in studying and analyzing previous studies related to big data by relying on books, periodicals, theses, and accounting standards related to the subject of the research. The researcher applied an inductive approach when conducting the field study and testing the statistical hypotheses related to the study of the relationship between the use of big data technology and firms’ financial performance. The findings show a correlation coefficient of (0.54) and a coefficient of determination of (48%), indicating that big data analytics positively affects the rate of return on assets, and that there is a statistically significant relationship between the advancement of accounting information systems and the enhancement of financial performance in big data technology, as measured by the rate of return on equity and the rate of return on assets, which have correlation rates of (0.53) and (42%), respectively. This relationship is reflected in the data on the existence of a statistically significant relationship between the use of big data technology and the enhancement of financial performance with big data technology. The intention of big data, as well as the absence of fundamental differences between the sample individuals, states that the use of big data technology leads to improved performance through the development of various accounting practices and good inventory management by predicting customer behaviour, thus increasing the competitiveness of competition and improving the reputation of the establishment on social media. This is reflected in the company’s sales and its survival in the market, as well as the development of analytical models and advanced methods of analysis that limit fraud and help control it, which is one of the establishment’s goals at present. This paper contributes to the literature by showing that the use of big data leads to a change in methods of preparing the final accounts, especially the financial position, and displaying them at fair value, which increases investor confidence. The study offers insights into the necessity of holding training courses for accountants concerning technology related to digital transformation and big data analysis for use in developing accounting practices.

The article describes the issues which appear during the storage and search in big unstructured data which is kept in cloud storage, as well as encryption mechanisms to optimize the search for such data and solutions used to process big unstructured data. The article is concerned with the technical and legal aspects of processing such data.

Design of Unstructured Big Data Matching System for Unidentified Aircraft Tracking Using Open CV

Employee satisfaction must be improved since it is essential to any organization's ability to succeed financially. This research aims to investigate the link between customer satisfaction and employee satisfaction as well as how each affects organizational success. The impact of several organizational elements on employee satisfaction is examined in this research. In this cohort study, qualitative research techniques were used. A self-administrated questionnaire with multiple choice and release ruined questions were used to gather the data. The results of the principal component analysis (PCA) based on the correlation matrix showed that among the cohorts examined, where consumers had also indicated satisfaction with the current services, there was a high level of employee (hotel staff) satisfaction. Customers' comfort and subsequent satisfaction have mostly been influenced by ambient cleanliness, wholesome meals, and hotel services. From the viewpoint of the workers, a positive work environment combined with rewards like pay and regular training motivated the staff to work devotedly to improve the organization, which is reflected in the customers' satisfaction levels. Our research supports the existence of an indirect relationship mediated by customers between organizational performance and employee happiness. In conclusion, it is plausible to assume that awareness of employee roles is crucial since it seems to be a crucial component of the success of contemporary organizations.

PurposeThe present research developed and investigated a multilevel theoretical framework by underpinning a resource-based view (RBV) that uncovers how and when academic researchers and organizational practitioners can strategically attain a competitive edge and increase their financial performance.Design/methodology/approachTo consider the dynamic and ever-challenging business environment, this research examined the contingency of big data analytics (BDA) and entrepreneurial orientation (EO). The multi-source and multilevel data used for this research were gathered from 195 manufacturing firms listed on the Pakistan stock exchange by employing a time-lagged design and survey strategy. Additionally, for hypothesis testing, multilevel path analysis was conducted in MPlus.FindingsThe authors uncovered the sequential mediation mechanisms of organizational innovation (OI) and competitive advantage (CA) on the relationship between individual-level employee innovative job performance (EIJP) and firms' financial performance (FFP) based on the resource-based view. The authors noted positive and significant sequential mediation of OI and CA on the effect of EIJP on FFP. Similarly, the relationship between EIJP and OI was found to be stronger when the boundary conditions of BDA and EO were higher than when they were lower.Originality/valueThis research offers new practical insights for managers and organizational practitioners to better value their EIJP, and due diligence is required when employing BDA and EO to attain a competitive edge over competitors and increase financial performance.