TRIPS Meets Big Data

This article explores the impact of Artificial Intelligence (AI) on the music industry, particularly focusing on the case of AI-generated covers. The emergence of AI technologies has been raising concerns not just about the originality and protection of AI-generated outputs but also about the complex input and training phase of those systems. The focus of this contribution is the latter, analysing the case of AI covers from the perspective of copyright and image rights. In the first part, an overview of the text and data mining (TDM) exception found in Article 4 of Directive 2019/790 is presented, with a primary focus on the opt-out mechanism in connection with the three-step test. Moving to the second part, the analysis delves into the complexities of voice cloning, highlighting the absence of a comprehensive European Union regime for image rights. By addressing these issues, this contribution unveiled two crucial points. First, AI models trained on various artists’ works to create and spread deepfake covers not only violate copyright but also reveal shortcomings in the TDM exception. Second, while the multifaceted image right regime may not be as exhaustive as necessary, it proves to be a viable solution against voice cloning with anticipated advancements in the future.

of works held in research and academic libraries and other relevant institutions, for research and private study; * clarify the current EU exception permitting the use of works that were made to be permanently located in the public space (the 'panorama exception'), to take into account new dissemination channels.

There is global attention on new data analytic methods. Data scraping (a typical first step for advanced data analytics), text and data mining (TDM, the extraction of knowledge from data) and machine learning (ML, often also simply referred to as Artificial Intelligence or AI) are seen as critical technologies. The legal issues involved in the regulation of data range from privacy and data protection (such as the GDPR) to proprietary approaches (such as copyright, database rights, or proposed new rights in data themselves). This paper focusses on one specific intervention, the introduction of two exceptions for text and data mining in the Directive on Copyright in the Digital Single Market (CDSM). Art. 3 is a mandatory exception for text and data mining (TDM) for the purposes of scientific research; Art. 4 permits text and data mining by anyone but with rightsholders able to “contract-out” (Art. 4), for example preventing TDM use of publicly available online content by “machine-readable means”. We trace the context of using the lever of copyright law to enable emerging technologies and support innovation. Within the EU copyright intervention, elements that may underpin a transparent legal framework for AI are identified, such as the possibility of retention of (permanent) copies for further verification. On the other hand, we identify several pitfalls, including an excessively broad definition of TDM which makes the entire field of data-driven AI development dependent on an exception. We analyse the implications of limiting the scope of the exceptions to the right of reproduction (which leaves the communication of research results in a grey zone). We also argue that the limitation of Art. 3 to certain beneficiaries remains problematic; and that the requirement of lawful access is difficult to operationalize. In conclusion, we argue that there should be no need for a TDM exception for the act of extracting informational value from protected works. The EU’s CDSM provisions paradoxically may favour the development of biased AI systems due to price and accessibility conditions for accessing training data that offer the wrong incentives. We also identify some old and new areas of the EU acquis which will play a crucial role in the future relationship of EU copyright law with technological innovation.

Innovations in science and technology boost-up the generation of an immense volume of digital data in the form of text, audio, video, images and paved the way for a new era called Big Data. A majority of this data is generated as a semi-structured and unstructured format poses a challenge for Natural Language Processing (NLP) and text analytics. This chapter seeks to layout the text mining perspective of big data analytics with an emphasis on applications in biomedicine and healthcare. The chapter aims to provide a better understanding of areas such as Big Data, Text Mining, Data Mining, Natural Language Processing and Machine Learning and its inter linkage. The chapter illustrates phases and task of text mining in big data scope. Finally, the chapter ends with a description of the two application areas biomedicine and healthcare where text mining using big data analytics will be used.

The area of fraud detection has been traditionally correlated with data mining and text mining. Even before the "big data" phenomena started in 2008, text mining and data mining were used as instruments of fraud detection. However, the limited technological capabilities of the pre-big data technologies made it very difficult for researchers to run fraud detection algorithms on large amounts of data. This paper reviews various existing learning methods for financial fraud detection of credit card fraud across different areas and find out their strengths and weaknesses. This paper has developed a credit card fraud detection system using XGBoost (eXtreme Gradient Boosting) method. In the proposed system, data imbalance problem has also been resolved by using SMOTE. Results are evaluated and compared with many states of art methods & found that the presented method performs well in achieving more accurate results after resolving data imbalance.

DEFINING TEXT MINING Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a suite of analysis tools. In a manner analogous to data mining, text mining seeks to extract useful information from data sources through the identification and exploration of interesting patterns. In the case of text mining, however, the data sources are document collections, and interesting patterns are found not among formalized database records but in the unstructured textual data in the documents in these collections. Certainly, text mining derives much of its inspiration and direction from seminal research on data mining. Therefore, it is not surprising to find that text mining and data mining systems evince many high-level architectural similarities. For instance, both types of systems rely on preprocessing routines, pattern-discovery algorithms, and presentation-layer elements such as visualization tools to enhance the browsing of answer sets. Further, text mining adopts many of the specific types of patterns in its core knowledge discovery operations that were first introduced and vetted in data mining research. Because data mining assumes that data have already been stored in a structured format, much of its preprocessing focus falls on two critical tasks: Scrubbing and normalizing data and creating extensive numbers of table joins. In contrast, for text mining systems, preprocessing operations center on the identification and extraction of representative features for natural language documents.

Data Mining refers to a set of methods applicable to large and complex databases to eliminate the randomness and discover the hidden pattern. Datamining (DM), also known as knowledge discovery from databases (KDD), is the extraction of new knowledge from huge databases. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Data mining tools can forecast the future trends and activities to support the decision of people. The scope of datamining is associated with Uncovering trends and patterns are a great power for the businesses of all sectors and industries. Modern intrusion detection applications are confronted with a variety of issues. These applications must be reliable, extensible, manageable, and minimal in maintenance costs. Data mining-based intrusion detection systems (IDSs) have shown high accuracy, good generalisation to novel types of intrusion, and stable behaviour in a changing environment in recent years. The number of hidden layers in various neural network topologies is evaluated in order to discover the best neural network. The technique of attempting to discover instances of network attacks by comparing current behaviour to the expected actions of an intruder is known as misuse detection. Artificial neural networks have the ability to detect and classify network activity using data that is limited, incomplete, and nonlinear. The main purpose of this work is to identify privacy and security concerns among cloud computing participants and consumers in a distributed environment. Techniques like Machine Learning, Natural Language Processing (NLP), and Data Mining are combined to automatically identify and uncover patterns from many sorts of materials. Predictive analytics is capable of dealing with both continuous and discontinuous changes. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics. The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). The use of data mining techniques based on Cloud computing will enable users to retrieve meaningful information from virtually integrated data warehouses, lowering infrastructure and storage costs. Data mining can extract useful and potentially useful information from the cloud. Big Data is typically defined by three characteristics known as the 3Vs (Volume, Velocity and Variety). The surveys approaches, environments, and technologies in key areas for Big Data analytics capabilities and discusses how they aid in the development of analytics solutions for Clouds. The clustering technique belongs to an unsupervised learning and it is used to discover a new set of categories. Grid-based clustering has the shortest processing time, which is typically determined by the size of the grid rather than the data. We compare the performance of three clustering algorithms: hierarchical clustering, density-based clustering, and K Means clustering. The majority of current approaches to detecting misuse involve the use of rule-based expert systems to identify indicators of known attacks. We provide a brief overview of the use of various Artificial Intelligence techniques and their advancements in the design, development, and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders. The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques.

Challenges and Techniques for Testing of Big Data

This study explores how to use big data analysis technology to conduct a comprehensive, objective and in-depth assessment of the communication effect of China’s foreign discourse culture, with a view to optimizing the communication strategy and enhancing the effectiveness of international communication. This study systematically comprehends the theory of foreign discourse culture and soft power, the application of big data technology in the field of cultural communication, the current status of domestic and international research on the assessment of the communication effect of foreign discourse culture, as well as the construction of the theoretical framework for the assessment of the communication effect of foreign discourse culture based on big data and the setting of the research hypotheses. This study collects big data resources from various Internet media platforms such as social media, news media reports, international public opinion databases, online forums and blogs, and carries out data cleansing and standardization to lay a data foundation for the subsequent analysis. This study adopts two major data mining techniques, text mining and social network analysis, as well as a variety of data mining algorithms, such as topic modeling, sentiment analysis, and influence calculation, to conduct in-depth mining and analysis of the data on the cultural communication of foreign discourses, and develops a communication path recognition system for tracking and recording the trajectory of the dissemination of foreign discourses and constructing a communication network mapping. In addition, this paper demonstrates the effect of the model through specific cases. It is found that through the deep mining of big data, we can intuitively see the degree of influence of different discourse contents, communication channels, time nodes, and other factors on the communication effect, thus providing a scientific basis and decision-making support for policy makers and communication practitioners.

In Article 3 of the “Proposal for a Directive on copyright in the Digital Single Market COM(2016) 593 final” the European Commission suggests an exception for text and data mining (TDM). While, in principle, a clear legal framework for TDM is to be welcomed, the proposed provisions are to be criticized regarding their scope and the applied regulatory method. This Position Statement develops an alternative proposal: Since TDM is to be seen as a normal use of works and other protected subject-matter, a field exemption is suggested allowing everyone to carry out TDM related to lawfully accessible works or other subject-matter. This includes the permission to extract contents of databases and to make reproductions for the sole purpose of TDM. Moreover, research organizations also need to carry out TDM regarding content to which they do not have lawful access. The proposal includes a specific provision obliging rightholders who market works or other subject-matter primarily for research purposes to provide datasets suitable for TDM only, for which they may request a reasonable payment.

Text mining applies the same analytical functions of data mining to the domain of textual information, relying on sophisticated text analysis techniques that distill information from free-text documents. IBM’s Intelligent Miner for Text provides the necessary tools to unlock the business information that is “trapped” in email, insurance claims, news feeds, or other document repositories. It has been successfully applied in analyzing patent portfolios, customer complaint letters, and even competitors’ Web pages. After defining our notion of “text mining”, we focus on the differences between text and data mining and describe in some more detail the unique technologies that are key to successful text mining.

The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers’ comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics.

Recent years have witnessed a revolution in data science and ‘big data’ into which psychiatric research has also being drawn. ‘Big data’ has been defined as data sets which are so large in size, so fast to change, and so complex in structure that traditional data processing techniques are overwhelmed [1]. The mining and exploitation of such big data resources as Electronic Healthcare Records (EHRs) present an exciting challenge to the field of psychiatric epidemiology. The number of big data projects within psychiatric research is growing, and Stewart and Davis’s literature review is, therefore, timely [2]. Technological advances in data processing and storage, computer networking, mobile technology, and data manipulation have rendered huge quantities of healthcarerelated data potentially amenable to analysis. Such studies potentially offer much larger patient numbers, wider parameters of study, and longer timescales of follow-up, than is typical of randomised controlled trials (RCTs) or cohort studies. A further advantage of this data is it often arises from naturalistic clinical settings, in terms of both clinical practice and patient health and comorbidity. Indeed, while often considered the ‘gold standard’ in medical research, RCTs do have important limitations, such as overly-strict exclusion criteria. Routine clinical data sets can, therefore, be complementary to RCT data, while also making research findings more relevant to everyday clinical practice. In addition, the quantity of clinical ‘big data’ potentially allows analysis of rarer clinical conditions, or subject areas that would be unlikely to meet ethical approval for more conventional studies (for example, medication usage in pregnancy). Big data studies provide the scale and breadth of patient numbers required for stratified, predictive, and personalised medicine research. In addition, notwithstanding many challenges of working with big data, large-scale analysis of routinely collected healthcare data sets has already demonstrated effectiveness in fields such as pharmacovigilance [3] and post-marketing clinical trials. As described in detail by Stewart and Davis, the challenging aspects of working with ‘big data’ are captured by the taxonomy of ‘Vs’—volume, velocity, and variety—first described by Laney [4] and extended since [1, 2]. There are additional issues to working with healthcare big data specifically, including that clinical administrative data are generally not collected, curated, or formatted in a manner optimised for research, and the inherent sensitivity of the data in terms of personal privacy. As the possibilities for healthcare big data expand into such areas as text mining and natural language processing of clinical records, near ‘real-time’ updating of repositories, and incorporation of new data streams from mobile and wearable technology, robust systems must be in place to channel the potential data deluge. How great a role ‘cloud’ computing and storage will have in this is an intriguing question. In healthcare big data, the benefits offered by the cloud in increased power and accessibility, and reduced cost, must be weighed against concerns over data ownership, encryption, and unauthorized access. An additional question is whether unique patient identifiers (UPIs) utilised by some national healthcare systems can be extended more This comment refers to the article available at doi:10.1007/s00127016-1266-8.

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.

There is a huge amount of data creating during the fourth industry revaluation and the data are generating explosively by various fields of the Internet of Things (IoT). The organizations are producing and storing the huge amount of data into the data servers every moment. This data comes from social media, sensors, tracking, website, and online news articles. The Google, Facebook, Walmart, and Taobao are the most remarkable organizations are generating most of the data in the web servers. Data comes into three forms as structured (text/numeric), semi structured (audio, video, and image) and unstructured (XML and RSS feeds). A business makes revenue from the analysis of 20% of such data, which is a structured form while 80% of data is unstructured. Therefore, unstructured data contains valuable information that can help the organization to improve the business productive, better decision-making, extract the insights, new products and services and understand the market conditions in various fields such as shopping, finance, education, manufacturing, and healthcare. The unstructured data are needed to be analyzed and distribute in a structured manner, that is required information’s are to be gathered through the data mining techniques are used to mining the data. In this paper, expose the importance of data analytics and data management for beneficial usage of business intelligence, big data, data mining and machine and data management. In addition, the different techniques that can be used to discover the knowledge and useful information from such data been analyzed. This can be beneficial for numerous users concern on text mining and convert complex data into meaningful information for researchers, analyst, data scientist, and business decision makers as well.