Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data; in contrast, data mining uses machine-learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.
Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.
Health informatics is information engineering applied to the field of health care, essentially the management and use of patient healthcare information. It is a multidisciplinary field that uses health information technology (HIT) to improve health care via any combination of higher quality, higher efficiency, and new opportunities. The disciplines involved include information science, computer science, social science, behavioral science, management science, and others. The NLM defines health informatics as "the interdisciplinary study of the design, development, adoption and application of IT-based innovations in healthcare services delivery, management and planning". It deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and bio-medicine. Health informatics tools include computers, clinical guidelines, formal medical terminologies, and information and communication systems, among others. It is applied to the areas of nursing, clinical medicine, dentistry, pharmacy, public health, occupational therapy, physical therapy, biomedical research, and alternative medicine, all of which are designed to improve the overall of effectiveness of patient care delivery by ensuring that the data generated is of a high quality.
Analytics is the discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making, within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Cheminformatics is the use of computer and informational techniques applied to a range of problems in the field of chemistry. These in silico techniques are used, for example, in pharmaceutical companies and academic settings in the process of drug discovery. These methods can also be used in chemical and allied industries in various other forms.
The ISO/TC 215 is the International Organization for Standardization's (ISO) Technical Committee (TC) on health informatics. TC 215 works on the standardization of Health Information and Communications Technology (ICT), to allow for compatibility and interoperability between independent systems.
Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.
Customer analytics is a process by which data from customer behavior is used to help make key business decisions via market segmentation and predictive analytics. This information is used by businesses for direct marketing, site selection, and customer relationship management. Marketing provides services in order to satisfy customers. With that in mind, the productive system is considered from its beginning at the production level, to the end of the cycle at the consumer. Customer analytics plays an important role in the prediction of customer behavior.
Academic analytics is basically defined as the process of evaluating and analysing organisational data received from university systems for reporting and decision making reasons. According to Campbell & Oblinger (2007), accrediting agencies, governments, parents and students are all calling for the adoption of new modern and efficient ways of improving and monitoring student success. This has ushered the higher education system into an era characterised by increased scrutiny from the various stakeholders. For instance, the Bradley review acknowledges that benchmarking activities such as student engagement serve as indicators for gauging the institution’s quality.
Drug discovery and development requires the integration of multiple scientific and technological disciplines. These include chemistry, biology, pharmacology, pharmaceutical technology and extensive use of information technology. The latter is increasingly recognised as Pharmacoinformatics.
IBM SPSS Modeler is a data mining and text analytics software application from IBM. It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming. One of its main aims from the outset was to get rid of unnecessary complexity in data transformations, and to make complex predictive models very easy to use. The first version incorporated decision trees (ID3), and neural networks (backprop), which could both be trained without underlying knowledge of how those techniques worked.
"Big data" is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed with big data are veracity and value.
Forest informatics is the combined science of Forestry and informatics, with a special emphasis on collection, management, and processing of data, information and knowledge, and the incorporation of informatic concepts and theories specific to enrich forest management and forest science; it has a similar relationship to library science and information science.
Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics.
Translational bioinformatics (TBI) is an emerging field in the study of health informatics, focused on the convergence of molecular bioinformatics, biostatistics, statistical genetics and clinical informatics. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients. Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. TBI employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes.
Industry 4.0 is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing and cognitive computing. Industry 4.0 is commonly referred to as the fourth industrial revolution.
Health care analytics is the healthcare analysis activities that can be undertaken as a result of data collected from four areas within healthcare; claims and cost data, pharmaceutical and research and development (R&D) data, clinical data, and patient behavior and sentiment data (patient behaviors and preferences,. Health care analytics is a growing industry in the United States, expected to grow to more than $31 billion by 2022. The industry focuses on the areas of clinical analysis, financial analysis, supply chain analysis, as well as, fraud and HR analysis.
Industrial big data refers to a large amount of diversified time series generated at a high speed by industrial equipment, known as the Internet of thingsThe term emerged in 2012 along with the concept of "Industry 4.0”, and refers to big data”, popular in information technology marketing, in that data created by industrial equipment might hold more potential business values. Industrial big data takes advantage of industrial Internet technology. It uses raw data to support management decision making, so to reduce costs in maintenance and improve customer service. Please see intelligent maintenance system for more reference.
Artificial intelligence (AI) in healthcare is the use of complex algorithms and softwares to estimate human cognition in the analysis of complicated medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input.
