Guest Editorial: Big data-driven theory building: Philosophies, guiding principles, and common traps

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

Chapter 21 - Big Data Integration

The purpose of the work is to study the current problems and prospects of the solution for processing big data received or stored in the Internet (web data), as well as the possibility of practical realization of Data Mining technology for big web data on practical example. Materials and methods. The study included a review of bibliographic sources on big data analysis problems.Data Mining technology was used to analyze large web data, as well as computer modeling of a practical problem using the C # programming language and creating a DDL database structure for accumulating web data.Results. In the course of the work, the specifics of big data were described, the main characteristics of big data were highlighted, and modern approaches to processing big data were analyzed. A brief description of the horizontal-scalable architecture and the BI-solution architecture for big data processing is given. The problems of processing large web data are formulated: limiting the speed of access to data, providing access via network protocols through general-purpose networks.An example showing the approach to processing large web data was also implemented. Based on the idea of big data, the described complexities of web data processing and the methods of Data Mining, techniques were proposed for effectively solving the practical problem of processing and searching patterns in a large data array.The following classes have been developed in the C # programming language:Class of receiving web data via the Internet; Data conversion class;Intelligent data processing class;Created DDL script that creates a structure for the accumulation of web data.A single UML class diagram has been developed.The constructed system of data and classes allows to solve the main part of the problems of processing large web data and perform intelligent processing using Data Mining technology in order to solve the problem posed of identifying certain records in a large array. The combination of object-oriented approach, neural networks and BI-analysis to filter data will speed up the process of data processing and obtaining the result of the studyConclusion. According to the results of the study, it can be argued that the current state of technology for analyzing large web data allows you to efficiently process data objects, identify patterns, get hidden data and get full-fledged statistical data.The obtained results can be used both for the purpose of the initial study of big data processing technologies, and as a basis for developing an already real application for analyzing web data. The use of neural networks and the created universal classes-handlers makes the created architecture flexible and self-learning, and the class declarations and the base DDL structure will greatly simplify the development of program code.

Theory building with big data-driven research – Moving away from the “What” towards the “Why”

Big data analysis technology can be used to transform massive data of oil production engineering into knowledge that can be used to guide oilfield production. Due to the lack of suitable large data analysis tools, a large data analysis software system was developed to simplify the workload of data analysis. This paper illustrates the research and application of large energy consumption data analysis through the development of big data analysis methods, process improvement and software platform, then achieves the goal of reducing costs and improving efficiency.

The purpose of the workis to explore the current problems and prospects of mining solution, big web data in real time, as well as the possibility of practical implementation of Web Mining technology for big web data on a practical example.Materials and methods.The study included a review of bibliographic sources on big data mining. We used Web Mining technology for associative analysis of large web data, as well as computer modeling of the practical task of transaction analysis using a general-purpose scripting language (PHP).Results.During the work, the specifics of the Data Mining technology were described, and a modern approach to the analysis of large web data –Web Mining was analyzed. A brief classification of tasks solved using Web Mining technology is given. The problem of data mining of large web data in a general-purpose scripting language (PHP) has been solved: the lack of libraries for data mining, the difficult normalization of data to the form necessary for associative analysis, interaction with the database management system. Also, an example showing an approach to the mining of large web data was implemented. Based on the understanding of Web Mining technology and the described difficulties of analyzing web data in the PHP language, methods for effectively solving the practical problem of analyzing web data based on transactions committed in a dynamic web application have been proposed. A module for associative analysis of customer transactions in the programming language PHP was developed. The module includes an intelligent data processing class. The structural scheme of the module and system architecture were developed. The constructed module allows us to solve the main part of the problem of associative analysis of large web data using Web Mining technology in order to solve the problem of identifying patterns in a large array of web data. Associative analysis of web data is much faster because of the combination of a general-purpose scripting language and an object-oriented approach.Conclusion.According to the results of the study, it can be argued that the current state of the technology for the analysis of large web data allows efficiently process data objects, identify patterns, obtain hidden data and receive complete statistical data in real time. The results can be used both for the purpose of the initial research of technologies for analyzing large web data, and as an addition to the content management system for the intelligent analysis of web data. The usage of the technology of associative analysis and the created universal handler class makes the created module flexible, while the possibility of manual integration makes this module universal. With manual integration, the database management system is not important. Algorithm methods work with selected data. This factor greatly simplifies the further development of program code.

This study investigates the effect of using deductive and inductive approach in teaching English to students on their conditional sentence mastery. This study also intends to describe the students’ perception toward deductive or inductive teaching approach. This study was a queasy experimental study which compares the effectiveness of deductive and inductive approach in teaching conditional sentences. The sample of this study is 64 SMK students in the academic years of 2012-2013. The students consist of two classes and each class comprises 32 students. The findings indicate that inductive teaching approach is more effective than deductive approach when used to teach conditional sentences. The result of the questionnaire indicates that students like the inductive approach and they are satisfied taught using inductive approach

Big Data OverviewDriven by the need to generate business value, the enterprise has started to adopt Big Data as a solution, migrating from the classical databases and data stores which lack the flexibility and are not optimized enough [1].The changes in the environment make big data analytics attractive to all types of organizations, while the market conditions make it practical. The combination of simplified models for development, commoditization, a wider palette of data management tools, and low-cost utility computing has effectively lowered the barrier to entry. [2]. The concept addresses large volumes of complex data, rapid growing data sets that may come from different autonomous sources.In recent approaches, Big Data is characterized by principles known as the 4VVolume, Variety, Velocity and Veracity [3]. There are opinions about accepting other principles as Big Data characteristics, such as Value.Each day more businesses realize that Big Data is relevant as the applications generate large volumes of data generated automatically, from different data sources, centralized or autonomous. As traditional databases hit limitations when the need of analyzing this data, dedicated solutions must be considered.Important BigData Solutions:* Apache HBase/Hadoop is based on Google's BigTable distributed storage system, which runs on top of Hadoop as a distributed and scalable big data store. This means that HBase can leverage the distributed processing paradigm of the Hadoop Distributed File System (HDFS) and benefit from Hadoop's MapReduce programming model. It combines the scalability of Hadoop with real-time data access as a key/value store and deep analytic capabilities of Map Reduce [4].HBase allows to query for individual records as well as derive aggregate analytic reports across a massive amount of data. It can host large tables with billions of rows, millions of columns and run across a cluster of commodity hardware. HBase is composed of three types of servers in a master slave type of architecture. Region servers are responsible to serve data for reads and writes. When accessing data, clients communicate with HBase Region Servers directly. Region assignment, DDL (create, delete tables) operations are handled by the HBase Master process.* Apache Cassandra is a distributed database used for the administration and management of large amounts of structured data across multiple servers, while providing highly available service and no single point of failure. It provides features such as continuous availability, linear scale performance, data distribution across multiple data centers and cloud availability zones. Cassandra inherits its data architecture from Google's BigTable and it borrows its distribution mechanisms from Amazon's Dynamo.The nodes in a Cassandra cluster are completely symmetrical, all having identical responsibilities. Cassandra also employs consistent hashing to partition and replicate data. it has the capability of handling large amounts of data and thousands of concurrent users or operations per second across multiple data centers.Cassandra has a hierarchy of caching mechanisms and carefully orchestrated disk I/O which ensures speed and data safety. Write operations are sent first to a persistent commit log (ensuring a durable write), then to a write-back cache called a memTable; when the memTable fills, it is flushed to a sorted string table - SSTable - on disk. A Cassandra cluster is organized as a ring, and it uses a partitioning strategy to distribute data evenly.* Redis represents an in-memory data structure store used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperlogs and geospatial indexes with radius queries. Redis stores all data in RAM, allowing lightning fast reads and writes. It runs extremely efficiently in memory and handles high-velocity data, needing simple standard servers to deliver millions of operations per second with submillisecond latency. …

The new technology helps collect and store a considerable amount of information about consumers’ preferences and decisions in real-time. Thus, the unprecedented volume, speed and variety of primary data, Big Data, are available from all sources and individual consumers. The term “Big Data” is now widespread and accepted globally. The term Big Data has become increasingly known and used in many industries. Big Data is a new source of product development ideas, customer service, shelf location, distribution, dynamic pricing, etc. Big Data will have an impact on almost every area of marketing. Firms that do not develop the resources and capabilities to use Big Data capabilities effectively will be challenged to develop a sustainable competitive advantage to survive the Big Data revolution. Consumer analysis is at the heart of a Big Data revolution. Marketing specialists are beginning to recognize the importance of Big Data as the new capital and that access to Big Data offers a company new ways to differentiate its products. In this article, exploratory research has been conducted to highlight areas where Big Data allows organizations to fundamentally know about their business and translate it into better decision-making and improved performance in marketing and consumer behavior. Moreover, a global analysis (based on public data) was performed using large volumes of data in marketing and the main reasons they were stored and used.KeywordsBig DataMarketingBig Data’s growth

The volume of the coming data in HEP is growing. The volume of the data to be held for a long time is growing as well. Large volume of data – big data – is distributed around the planet. The methods, approaches how to organize and manage the globally distributed data storage are required. The distributed storage has several examples for personal needs like own-cloud.org, pydio.com, seafile.com, sparkleshare.org. For enterprise-level there is a number of systems: SWIFT - distributed storage systems (part of Openstack), CEPH and the like which are mostly object storage. When several data center’s resources are integrated, the organization of data links becomes very important issue especially if several parallel data links between data centers are used. The situation in data centers and in data links may vary each hour. All that means each part of distributed data storage has to be able to rearrange usage of data links and storage servers in each data center. In addition, for each customer of distributed storage different requirements could appear. The above topics are planned to be discussed in data storage proposal.

PurposeThe purpose of this paper is to map corporate disclosure theories as a step towards filling a gap in the theoretical background for corporate disclosure research. The purpose of the map is to encompass a range of particular theories relating to corporate disclosure and to demonstrate the complex relationships between different notions of the financial disclosure phenomenon. This will help new researchers to understand how particular corporate disclosure theories are related, as well as help with teaching accounting theories at undergraduate and postgraduate level.Design/methodology/approachA deductive and inductive approach to theory building was applied. The deductive approach suggests identifying the gap in the literature, the inductive approach then prescribes theory building in three stages: phenomenon observation, categorisation and relationship building. This approach serves to develop a theoretical map integrating the corporate disclosure theories.FindingsThe paper discusses theories that recognise actual features of financial markets – market failure, information asymmetry and adverse selection – to provide an explanation for the existence of corporate reporting regulations and managerial incentives, which control and determine the maximum level of corporate information under these conditions. It then integrates these theories in a map seeking to explain corporate disclosure levels, mandatory and voluntary, financial and narrative. A combination of theoretical supplements – codification theory, Dye's theory of mandatory and voluntary disclosure, and disclosure transformation theory – are proposed in this framework as theories to explain processes of change in mandatory and voluntary corporate disclosure in practice.Originality/valueAnother benefit mapping these theories is to provide useful insights into existing disclosure theories, which may help to explain why some empirical results have been inconsistent with the predictions of these theories. No similar attempts have been published in the accounting literature.

At this time big data, with large data volumes that continue to grow and become complex, is a challenge and opportunity for governments and organizations. This is said to be a challenge considering that the increase in data volume will produce a lot and variety of information, thus requiring efficient ways of storing and managing data. Big data is said to be an opportunity considering that the magnitude of the study results and proper information management will open up new opportunities to recognize the potential for improving services in various government and organizational sectors. Big data can open up opportunities to increase knowledge about various aspects of people's lives so that it can encourage the creation of new trends and innovations in various fields, improve and optimize operations, and determine future conditions so that governments and organizations can produce appropriate basic products. In the future, big data will change the way governments and organizations run their operations, creating challenges and opportunities for decision-makers. Even so, in the future big data can be utilized more optimally for the benefit of the wider community. In the future, big data has great potential to be used in many sectors to improve people's welfare. By using the right methods, big data can be very valuable in improving the welfare and quality of life of the wider community. Therefore, to be able to implement big data to improve the welfare and quality of life of the wider community, it is necessary to understand the challenges and opportunities of big data in the development of digital technology.

The advent of information and communication technologies (ICT) ushers a cost-effective prospect to take care of large volumes of complex data, commonly known as “big data” in the supply chain operational environment. Big data is being generated today by web applications, social media, intelligent machines, sensors, mobile phones, and other smart handheld devices. Big data is characterized in terms of the velocity, volume, and variety with which it produces along the supply chain. This is due to recent advances in telecommunication networks along with centralized and decentralized data storage systems, which are processed thanks to modern digital computational capabilities. There is a growing interest in the use of this large volume of data and advanced analytics for diverse types of business problems in supply chain management (SCM). Such decision-support software applications employ pure mathematical techniques, artificial intelligence techniques, and sometimes uses both techniques to perform analytical operations that undercover relationships and patterns within supply chain generated big data. This chapter proposes a framework for the utilization of big data in SCM decision making. The framework is based on the SCOR (supply chain operations reference) model, which is endorsed by Supply Chain Council (SCC). The proposed framework is influenced by the enterprise potential of augmented reality and virtual reality in supply chain applications, and it identifies key categories of big data analytics applications for the key businesses of SCOR model. Finally, the chapter highlights research issues to extract insight from big data sources for enterprise decision making.

Artificial intelligence (AI) has proved itself to be a very powerful marketing tool and thus has become one of the most popular buzzwords in the industry. AI has been in the market for decades, but its recent popularity can be attributed to availability of cheap, scalable computational power, growth in big data and development of new AI technology. In the past, AI was plagued with problems such as availability of data (as it takes a large volume of data in order to make the machine learn), but with the advent of Internet and its availability, acquiring the data became very easy. Although big data was available, it took a lot of time to train the machines, but the development of new high-performing computational systems made it easier, faster, as well as cheaper. The big four tech companies Google, Amazon, Apple and Facebook are already using AI in their daily operations, such as Amazon’s recommendation systems, Google’s pairing of advertisement with the content being searched for etc. Currently, AI is being seen as the next big thing in marketing as it is expected to make marketing more effective by providing managers with insights and information which was not assessable before and speeding up decision-making processes. Recently there has been much academic research into marketing where AI can be used. For example, with the help of AI, marketers can better understand e-word of mouth (eWOM) by applying techniques like text mining, and by using the latest advancements in programming this has created models of direct marketing responses, predicting churn (the rate at which customers stop using a product) using classification trees and training machines to better understand customer needs etc. But there is still a long way to go for AI when it comes to solving marketing’s varying problems. Marketers needs to have better understanding of AI in order to comprehend how it can be better implemented to solve prevailing problems in the field, and this area is not yet well researched.

Reducing data volume in big data: Parallel processing based data filtering techniques Professor Shikharesh Majumdar from Carleton University examines reducing data volume in big data, focusing on parallel processing based data filtering techniques. Volume, velocity, and variety are three well-known characteristics of big data. The large data volumes often introduce formidable challenges to processing such data in a timely and economical manner. Research on data filtering is underway under the leadership of Shikharesh Majumdar at the Real Time and Distributed Systems Research Centre at Carleton University. Users are often interested only in a subset of the raw data.