Search

Batch processing refers to an organization of work in which cases are synchronized such that they can be processed as a group. Prior research has studied batch processing mainly from a deductive angle, trying to identify optimal rules for composing batches. As a consequence, we lack methodological support to investigate according to which rules human resources build batches in work settings where batching rules are not strictly enforced. In this paper, we address this research gap by developing a technique to inductively mine batch activation rules from process execution data. The obtained batch activation rules can be used for various purposes, including to explicate the real-life batching behavior of human resources; to determine the compliance between the prescribed and actual batching behavior; or to investigate the influence of alternative batching behavior on service levels. The evaluation of our technique using both synthetic and real-world data demonstrates its effectiveness. With this work we complement prescriptive research on batch processing with a descriptive technique that is empirically grounded in process execution data. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of process discovery from large and complex event logs. We depart from the existing literature and formulate the problem of optimal process discovery. A formal mathematical programming model is given based on a novel hierarchical structuration of the event logs. Desired properties of event trace score functions are described, and the properties of optimal process models are proved. A combination of Monte Carlo optimization and tabu search is proposed to overcome the complexity related to the huge size of the event logs and the combinatorial solution space. Numerical results show that our approach is suitable for large event logs and that it performs better than the state-of-the-art approaches. We also demonstrate the applicability of our method on a real case study in health care. This paper illustrates the benefits of combining techniques from the operational research and the process mining fields. Note to Practitioners—Though directly applicable to general business process discovery, this paper is motivated by our collaboration with the company HEVA (Lyon, France) and health practitioners on patient care pathway discovery. The French hospitalization database that contains hospitalization history of all patients is used for this purpose and our goal is to determine the most meaningful process model of the patient hospitalization history. The hierarchical event structure of this paper provides a natural way of representing relationship between hospitalization events and is readily obtainable from the classical ICD-10 codes. The formal mathematical model and our optimization algorithm allow the end users to best balance between the faithfulness of the process model and its complexity. A case study of cardiovascular patients is presented to show the capability of the proposed approach to clearly capture the major patient pathways before and after the implementation of defibrillators. The results of this paper are highly valuable for doctors and public health decision makers, as crucial information is provided on patient care pathways for any selected cohort. [ABSTRACT FROM AUTHOR]

Copyright of International Dialogue: East-West is the property of International Center for Slavic Education- Sveti Nikole and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, process mining is becoming a growing area of interest in business process management (BPM). Process mining consists in the extraction of information from the event logs of a business process. From this information, we can discover process models, monitor and improve our processes. One of the applications of process mining, is the predictive monitoring of business process. The aim of these techniques is the prediction of quantifiable metrics of a running process instance with the generation of predictive models. The most representative approaches for the runtime prediction of business process are summarized in this paper. The different types of computational predictive methods, such as statistical techniques or machine learning approaches, and certain aspects as the type of predicted values and quality evaluation metrics, have been considered for the categorization of these methods. This paper also includes a summary of the basic concepts, as well as a global overview of the process predictive monitoring area, that can be used to support future efforts of researchers and practitioners in this research field. [ABSTRACT FROM AUTHOR]

Abstract In the last decade there has been a large increase in corporate and public reliance on social media for information, rather than on the traditional news and information sources such as print and broadcast media. People freely express their views, moods, activities, likes/dislikes on social media about diverse topics. Rather than surveys and other structured data gathering methods, text data mining is now commonly used by businesses to go through their unstructured text in the form of emails, blogs, tweets, likes, etc. to find out how their customers feel about their company and their products/services. This paper reports upon a study using Twitter (recently renamed to “X”) data to determine if meaningful and actionable information could be gained from such social media data in regard to pandemic issues and how that information compares to a traditional survey. In early 2020, the COVID-19 pandemic hit and forced colleges to move classes to an online format. While there is considerable literature in regard to using social media to communicate geo-political issues and in particular pandemics, there is not a study using social media to explore public sentiment in regard to COVID’s forcing online education upon the public. In this study, text data mining was used to gain some insight into the feeling of Twitter users in regard to the effect of COVID-19 and the switch to online education in colleges. This study found that Twitter data mining did produce actionable information similar to the traditional survey, and the study is important since its results may influence organizations to explore the use of Twitter (and possibly other social media) to obtain people’s sentiments instead of (or in addition to) traditional surveys and other traditional means of gathering such information. This paper demonstrates both the process of text data mining social media and its application to current real-world issues.

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyze online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organizing strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets. [ABSTRACT FROM AUTHOR]

In the era of “big data”, one of the key challenges is to analyze large amounts of data collected in meaningful and scalable ways. The field of process mining is concerned with the analysis of data that is of a particular nature, namely data that results from the execution of business processes. The analysis of such data can be negatively influenced by the presence of outliers, which reflect infrequent behavior or “noise”. In process discovery, where the objective is to automatically extract a process model from the data, this may result in rarely travelled pathways that clutter the process model. This paper presents an automated technique to the removal of infrequent behavior from event logs. The proposed technique is evaluated in detail and it is shown that its application in conjunction with certain existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets. [ABSTRACT FROM PUBLISHER]

The recent attention for Big Data illustrates that organizations are aware of the potential of the torrents of data generated by today's information systems. Despite increasing interest regarding to Big Data and analytics tools, to the best of our knowledge, there are no studies cover and classify the types of research being published specifically on analytical intelligence in processes associated with data science tools. As a first step towards bridging this gap, we carried out a systematic mapping to synthesize an overview of the area. We went through 351 papers on theme. Among them, 63 were related to analytical intelligence in processes and only 38 met the criteria for inclusion and exclusion of articles defined in this study. These 38 papers were selected and categorized according to their contribution. As a result, a chart of the area was developed and the most investigated topics were identified indicating that most studies focus on investigating how analytical intelligence in processes can be used to discovery, conformance checking, model repair and enrich, role discovery, bottleneck analysis, monitoring of events, frauds audit, predicting the remaining flow time, and recommending next steps. [ABSTRACT FROM AUTHOR]

Mining is such as automated technology which facilitates us to find the relations between various parameters and predicts the future outcomes of business sector. Mining tools (data mining, text mining, and web mining) are used to foreseen the future, profit, investment relations in large databases for BI. In this paper, we review various data mining tools and techniques and their performance in BI sectors. Finally, we shall state some of the important factors to be considered while designing & developing a BI framework using data mining techniques for various sectors. Business Intelligence (BI) has turned out to be a predictable technological advantage in the last couple of years, for the large enterprises (especially Mobile Technology Ventures). Such ventures could afford to buy, implement and maintain BI solutions. Recently, small and medium size enterprises all over the globe have understood the competitive and financial benefits of BI. However, limited IT budgets of small companies and BI's high total cost of ownership have created a gap between large and small enterprises where small enterprises do not become fortunate to avail the virtues of BI because of the affordability factor. This Paper provides the systematic study and analysis of various factors (Data Mining techniques, BI framework and their effects on designing a successful automated decision making system). It presents the strong knowledge foundation so that one can give a proper justice to achieve BI objectives. [ABSTRACT FROM AUTHOR]

Data-intensive systems in e-governance collect and process data to ensure conformance to a set of business rules. Testers meticulously verify data in test databases, extracted from different steps of a live production stream , for correct application of business rules. We simplify the process by allowing testers to model a test domain on a relational database and automatically generate test cases representing data interactions satisfying combinatorial interaction coverage criteria. This paper also introduces test cases with self-referential interactions, which is a necessity in real-world databases. We verify these test cases using our human-in-the-loop tool, Depict. Depict, with expert assistance, generates complex SQL queries for test cases and produces a visual report of test case satisfaction. We apply the approach to two scenarios: 1) simplify and optimize a periodic archiving operation and 2) verify fault codes within the testing environment of the Custom directorate's TVINN system. [ABSTRACT FROM AUTHOR]

The data mining (DM) is a great task in the process of knowledge discovery from the various databases. In the corporate sectors, every system has the tough competition with the other system with respect to their value for the business and the financial improvement. Data mining, a dynamic and fast-expanding field, which applies the advanced data analysis techniques, from machine learning, statistics, artificial intelligence or database systems to find out relevant trends, patterns and relations present within the data, information impossible to observe manually. This paper presents the applications of data mining in the banking sectors. It contains a general overview of data mining, providing a definition of the concept, primary data mining techniques and mentioning the main fields for which the data mining can be applied. It also presents the banking business sector which can benefit from the use of DM tools, along with their use cases i.e., retail and insurance banking sector. Also the commercially available DM tools and their key features are discussed within the paper. With the analysis of DM and the business areas that can successfully apply it, it also presents the main features of the DM solution by using the DM software and data languages i.e., based on survey of users throughout the world that can help to improve the customer experiences and decision making that can be applied for the banking system and the architecture, with its main components for the solution, customer segmentation, banking profitability factor, marketing, risk management and customer relations management and the fraud detection. [ABSTRACT FROM AUTHOR]

Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this article, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data. [ABSTRACT FROM AUTHOR]

Text mining have gained great momentum in recent years, with user-generated content becoming widely available. One key use is comment mining, with much attention being given to sentiment analysis and opinion mining. An essential step in the process of comment mining is text pre-processing; a step in which each linguistic term is assigned with a weight that commonly increases with its appearance in the studied text, yet is offset by the frequency of the term in the domain of interest. A common practice is to use the well-known tf-idf formula to compute these weights. This paper reveals the bias introduced by between-participants’ discourse to the study of comments in social media, and proposes an adjustment. We find that content extracted from discourse is often highly correlated, resulting in dependency structures between observations in the study, thus introducing a statistical bias. Ignoring this bias can manifest in a non-robust analysis at best and can lead to an entirely wrong conclusion at worst. We propose an adjustment to tf-idf that accounts for this bias. We illustrate the effects of both the bias and correction with with seven Facebook fan pages data, covering different domains, including news, finance, politics, sport, shopping, and entertainment. [ABSTRACT FROM AUTHOR]

Due to the large volumes of empirical digitized data, a critical challenge is to identify their hidden and unobvious patterns, enabling to gain new knowledge. To make efficient use of data mining (DM) methods, it is required to know its capabilities and limits of application as a cognitive tool. The paper aims to specify the capabilities and limits of DM methods within the methodology of scientific cognition. This will enhance the efficiency of these DM methods for experts in this field as well as for professionals in other fields who analyze empirical data. It was proposed to supplement the existing classification of cognitive levels by the level of empirical regularity (ER) or provisional hypothesis. If ER is generated using DM software algorithm, it can be called the man-machine hypothesis. Thereby, the place of DM in the classification of the levels of empirical cognition was determined. The paper drawn up the scheme illustrating the relationship between the cognitive levels, which supplements the well-known schemes of their classification, demonstrates maximum capabilities of DM methods, and also shows the possibility of a transition from practice to the scientific method through the generation of ER, and further from ER to hypotheses, and from hypotheses to the scientific method. In terms of the methodology of scientific cognition, the most critical fact was established – the limitation of any DM methods is the level of ER. As a result of applying any software developed based on DM methods, the level of cognition achieved represents the ER level.

Purpose: The purpose of this research is to optimize the usefulness of sales transaction reports in Sriwijaya State Polytechnic (Polsri) foodcourt and extract sales transaction report data to become the knowledge that can be used in sales strategies that is recommendations for tenant positions in accordance with customer behavior by using association rules analysis. Design/methodology/approach: The approach using Knowledge Discovery in Database through stages (1) Data Selection; (2) Data Classification; (3) Raw Itemset Table; (4) Data Mining Process using Association Rules Analysis; (5) Analysis Result Interpretation; (6) Marketing Strategy Recommendation. Findings: The results of the study are recommendations for tenant positions in accordance with customer behavior tendencies Research limitations/implications: Polsri foodcourt sales transaction report used in the period 01 May 2019 – 31 July 2019, with a total of 153,401 data records, data analysis using Association Rules Analysis with A Priori Algorithm, tools used are Microsoft Excel and WeKa 3.6.10 Practical implications: The recommended tenant position can be used as a sales strategy that makes it easier for customers to choose the product to be purchased and increase sales rate Originality/value: The paper is original Paper type: Research paper

Separating decision modelling from the processes modelling concern recently gained significant support in literature, as incorporating both concerns into a single model impairs the scalability, maintainability, flexibility, and understandability of both processes and decisions. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group provides a suitable solution for externalising decisions from processes and automating decision enactments for processes. This article introduces a systematic way of tackling the separation of the decision modelling concern from process modelling by providing a Decision as a Service (DaaS) layered Service-Oriented Architecture (SOA) which approaches decisions as automated and externalised services that processes need to invoke on demand to obtain the decision outcome. The DaaS mechanism is elucidated by a formalisation of DMN constructs and the relevant layer elements. Furthermore, DaaS is evaluated against the fundamental characteristics of the SOA paradigm, proving its contribution in terms of abstraction, reusability, loose coupling, and other pertinent SOA principles. Additionally, the benefits of the DaaS design on process-decision modelling and mining are discussed. Finally, the DaaS design is illustrated on a real-life event log of a bank loan application and approval process, and the SOA maturity of DaaS is assessed. [ABSTRACT FROM AUTHOR]

Computing clouds have become the platform of choice for the deployment and execution of scientific workflows. Due to the uncertainty and unpredictability of scientific exploration, the execution plan for a scientific workflow may vary from the definition. It is therefore of great significance to be able to discover actual workflows from execution histories (event logs) to reproduce experimental results and to establish provenance. However, most existing process mining techniques focus on discovering control flow-oriented business processes in a centralized environment, and thus, they are mostly inapplicable to the discovery of data flow-oriented, unstructured scientific workflows in distributed cloud environments. In this paper, we present Scientific Workflow Mining as a Service ( \sf SWMaaS ) to support both intra-cloud and inter-cloud scientific workflow mining. The approach is implemented as a \sf ProM plug-in and is evaluated on event logs derived from real-world scientific workflows. Through experimental results, we demonstrate the effectiveness and efficiency of our approach. [ABSTRACT FROM PUBLISHER]

In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains. [ABSTRACT FROM PUBLISHER]

For various entering and transmission issues raised by human or system, missing events often occur in event data, which record execution logs of business processes. Without recovering the missing events, applications such as provenance analysis or complex event processing built upon event data are not reliable. Following the minimum change discipline in improving data quality, it is also rational to find a recovery that minimally differs from the original data. Existing recovery approaches fall short of efficiency owing to enumerating and searching over all of the possible sequences of events. In this paper, we study the efficient techniques for recovering missing events. According to our theoretical results, the recovery problem appears to be NP-hard. Nevertheless, advanced indexing, pruning techniques are developed to further improve the recovery efficiency. The experimental results demonstrate that our minimum recovery approach achieves high accuracy, and significantly outperforms the state-of-the-art technique for up to five orders of magnitudes improvement in time performance. [ABSTRACT FROM AUTHOR]

While the use of social media influencers (SMIs) by brands is becoming more widespread, the academic literature about SMI communication is still scarce. This is one of the first studies on SMI brand storytelling, using data mining and natural language processing to understand how SMIs tell brand stories on Instagram, what kinds of stories they tell, and the impact they have on follower engagement. The findings show that the "rise-fall" emotional arc was the most common story arc used by SMIs. In addition, SMIs frequently used the first-person perspective and featured themselves as the protagonists in their stories. Last, SMIs who used more first-person pronouns and more positive emotions in their stories received more "likes" than those who used fewer first-person pronouns and fewer positive emotions. The paper concludes with a discussion of the study's implications for SMI communication theory-building and practice as well as its limitations. [ABSTRACT FROM AUTHOR]

Internet has become integral part of day-to-day business to almost everybody, this result in diversified interests of customers. So as to catering to this intrinsic need, any E-Commerce firm to survive must be of cutting edge and competitive edge. The providers should not only to stay abreast with technologies where the life cycle of a technology is at its bare minimum and further dwindling They should also entertain the customers through inventively fine tuning the delicate parameters of website. This involves evaluating the usage pattern and trails of the customer left as a log, deriving pattern from click stream etc. However, the cutting edge technology applied by the big healthcare E-Commerce industries like private Cloud utilization (John et al., Optimization and Computing, 2012), web content mining enables them to attract and retain innumerable number of customers even during peak hours. According to the research carried out in this paper, there are two distinct types of online business based on web content promoted towards buy, they are classified as exhaustive promote and partial promote. Typically exhaustive promote website perform even complex web mining operations for identifying and enticing the potential customers to buy various healthcare products based on various factors such as buying habits, interests etc. However for the partial promote in the observed cases, they are not even aware of the existence of such techniques. Based on the analysis performed on various renounced online websites, if 60% and above of the web content leads the customer to perform the 'buy, then it is exhaustive promote the rest is considered as partial promote. Moreover a huge gap is observed between Partial and exhaustive promote when it comes to the deployment of the web mining techniques. Consequently to understand the varying role of web mining in the online business successes, this paper models the web mining as a Game in Cournot Model. The results show that the model suits the economics behind the online businesses in both the cases and thus helps to identify or enhance the underlying web mining techniques towards business success. [ABSTRACT FROM AUTHOR]

Collection of customer information is seen necessary for development of the marketing strategies. Developing technologies are used very effectively in bank marketing campaigns as in many field of life. Customer data is stored electronically and the size of this data is so immense that to analyse it manually with a team of human analysts is impossible. In this paper, data mining techniques are used to interpret and define the important features to increase the campaign’s effectiveness, i.e. if the client subscribes the term deposit. The bank marketing dataset from the University of California at Irvine Machine Learning Repository has been used for the proposed paper. We consider two feature selection methods namely Information Gain and Chi-square methods to select the important features. The methods are compared using a supervised machine learning algorithm of Naive Bayes. The experimental results show that reduced set of features improves the classification performance.

Background: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

Background: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we apply data mining and analytics to extract the usage patterns of social media by small businesses. Objectives: The aim of this paper is to describe with an example how data mining can be applied to social media. This paper further examines the impact of social media on small businesses. The Twitter posts related to small businesses are analyzed in detail. Methods/Approach: The patterns of social media usage by small businesses are observed using IBM Watson Analytics. In this paper, we particularly analyze tweets on Twitter for the hashtag #smallbusiness. Results: It is found that the number of females posting topics related to small business on Twitter is greater than the number of males. It is also found that the number of negative posts in Twitter is relatively low. Conclusions: Small firms are beginning to understand the importance of social media to realize their business goals. For future research, further analysis can be performed on the date and time the tweets were posted.

Serious concerns about the invasion of digital footprint information privacy due to intense commercial promotion through data mining has led to the emergence of privacy by design in the form of the Do Not Track (DNT) mechanism. This paper attempts to construct a theory to justify and find an appropriate solution to balance the different interests by implementing the DNT mechanism in the real-world marketing industry. The research method involves deduction in legal reasoning. This paper argues that digital footprint information privacy, which has high commercial value, should at least be awarded the status of a semi-fundamental human right. Additionally, when to adopt a DNT opt-out or default mechanism depends on the type of personal information involved. The practical implications suggest a compromise between digital footprint privacy protection and commercial applications in the marketing industry, to be achieved through technology. Since this important topic is relatively new in the area of marketing applications and no primary academic research has established a complete theoretical legal foundation, this article is among the first to do so. Beside its originality, this article also contributes to the literature by proposing a theoretically practical mechanism for both digital footprint privacy protection and marketing profits.

Copyright of Turkish Studies - Economics, Finance, Politics is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.

Purpose: The purpose of this paper is to present the results of an analysis and evaluation of data on employee turnover based on deep data mining using association rules and decision trees in a specific organisation. Methodology/Approach: For the analysis, we chose deep data mining methods, primarily a search for association rules using the Apriori algorithm in the R programming language. For the sake of supplementation and comparison of results, data were also analysed using the predictive decision trees method, applying the C5.0, rpart and ctree algorithms in the R program. Findings: The results of the analyses showed that observing the basic principles of correct communication from the beginning of an employment relationship, or during hiring, is justified. Communication and regular conversations between a superior and employees can help identify problems earlier, address them and reduce the number of people leaving the company. The results of the analysis helped the organisation to set measures to reduce the number of an employee leaving. Research Limitation/implication: A limiting factor in performing such analyses is the availability of quality data in the required quantity. Our most significant advantage when performing our analysis was that quality data were available. To create the final structure of the required data set, we used data from the organisation’s internal information systems. Originality/Value of paper: This contribution offers a new approach to analysing data on employee turnover, whose essence is that we need to find the most interesting and frequent correlations in a significant amount of data.

One of the promising areas of business analysis is the development of new methods and tools for accounting of nonfinancial and non-numeric information. There is a significant number of theoretical and practical solutions in this field; however, the issues of the transformation dynamics of companies’ business vocabulary need to be studied more extensively. The article aims to identify and interpret latent information reflecting strategic guidelines and conditions for the economic development of Russian enterprises. The methodology of the study is based on the concepts of narrative economics and multimodal business analytics, which is a system of scientific-practical methods for analyzing the activities of economic entities through the use of data from heterogeneous sources. The Data Mining methods and tools for analyzing and systematizing large volumes of textual information were used. The data for research were retrieved from the annual reports of the largest Russian companies for 2018–2020. Among the main indicators of the business vocabulary transformation considered in the paper are the occurrence of unique key tokens (UKTs) and the dynamics of its change, as well as the main contexts of UKTs relevant to the problem of development. The findings indicate noticeable changes in the vocabulary of Russian companies’ annual reports, such as a decline in covering formal aspects of economic activity and a growing debate on the development in the presence of risk. It is shown that these trends were most clearly manifested in the reports of metallurgical and energy enterprises. The research results can serve as a basis for enhancing the analytical and predictive effectiveness of modern business analysis.

Purpose - Personal bankruptcy is on the rise in Malaysia. The Insolvency Department of Malaysia reported that personal bankruptcy has increased since 2007, and the total accumulated personal bankruptcy cases stood at 131,282 in 2014. This is indeed an alarming issue because the increasing number of personal bankruptcy cases will have a negative impact on the Malaysian economy, as well as on the society. From the aspect of individual’s personal economy, bankruptcy minimizes their chances of securing a job. Apart from that, their account will be frozen, lost control on their assets and properties and not allowed to start any business nor be a part of any company’s management. Bankrupts also will be denied from any loan application, restricted from travelling overseas and cannot act as a guarantor. This paper aims to investigate this problem by developing the personal bankruptcy prediction model using the decision tree technique. Design/methodology/approach - In this paper, bankrupt is defined as terminated members who failed to settle their loans. The sample comprised of 24,546 cases with 17 per cent settled cases and 83 per cent terminated cases. The data included a dependent variable, i.e. bankruptcy status (Y = 1(bankrupt), Y = 0 (non-bankrupt)) and 12 predictors. SAS Enterprise Miner 14.1 software was used to develop the decision tree model. Findings - Upon completion, this study succeeds to come out with the profiles of bankrupts, reliable personal bankruptcy scoring model and significant variables of personal bankruptcy. Practical implications - This decision tree model is possible for patent and income generation. Financial institutions are able to use this model for potential borrowers to predict their tendency toward personal bankruptcy. Social implications - Create awareness to society on significant variables of personal bankruptcy so that they can avoid being a bankrupt. Originality/value - This decision tree model is able to facilitate and assist financial institutions in evaluating and assessing their potential borrower. It helps to identify potential defaulting borrowers. It also can assist financial institutions in implementing the right strategies to avoid defaulting borrowers.

Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign.

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency. [ABSTRACT FROM PUBLISHER]

Process mining aims at discovering behavioral knowledge of business processes from their event logs, which has received an increasing attention in the era of cloud computing and big data. Surprisingly, to date, discovering structural errors (e.g., deadlocks and lack of synchronization) from event logs has not been considered in state-of-the-art process mining techniques. Moreover, existing process discovery approaches cannot be directly applied to event logs of processes with structural errors due to erroneous event occurrences caused by unsynchronized activities. To address this problem, we first preprocess the event log to obtain two separate event logs that are used to discover deadlocks and lack of synchronization, respectively. Erroneous event occurrences caused by unsynchronized activities are discarded in the two processed event logs, from which our error mining algorithms can discover all process fragments involving structural errors, without the need to obtain the overall process first. We implement our approach in a ProM plugin and evaluate it on event logs of real-life business processes, the results of which demonstrate that our approach can effectively and efficiently discover deadlocks and lack of synchronization if event logs contain sufficient event sequences. [ABSTRACT FROM AUTHOR]

In the current business practice, recommender agents are widely used in e-commerce domain to actively recommend the right items to online users. Traditional Collaborative Filtering (CF) recommender systems are developed based on single ratings which are used to match similar users based on their past ratings. Although these types of recommender systems have been successfully implemented in healthcare context, however the use of multi-criteria CF for health product recommendation has been rarely explored. The aim of this paper is to propose a new recommendation method based on multi-criteria CF to enhance the predictive accuracy of recommender systems in healthcare domain using clustering, dimensionality reduction and prediction machine learning methods. To do so, we develop a knowledgebased system to predict the users' overall assessment value of health products using Mamdani's fuzzy inference technique. Accordingly, we used Classification and Regression Trees (CART) to discover the fuzzy rules to be used in the fuzzy rule-based technique. To improve the recommendation accuracy of proposed multi-criteria CF, we apply a clustering technique and ensembles of fuzzy rule-based prediction models. We also use a robust dimensionality reduction technique, Higher Order Singular Value Decomposition (HOSVD), to find the similar users and products in each cluster to solve the sparsity issue. We test the accuracy of recommendation method on two health products datasets with three criteria, Product Brand, Product Price and Product Quality, crawled from the online health products stores. Our experiments confirm that the proposed method can be a promising and effective intelligent system for healthcare products recommendation. [ABSTRACT FROM AUTHOR]

This paper provides a way to discover strong individual influences on investments in retirement accounts. Data are from SHARE-ERIC (Wave7). Principal residences in ex-communist countries or not and full-time education before 1989 served as filters. Two particular models with good classification accuracy resulted based on data mining, variable selection methods, and logistic regressions. A statistical script generated tables with comparable coefficients (average marginal effects). Common influences from the same financial category as the outcome emerged (having life insurance or ever investing in mutual funds or stocks). The younger respondents, those with computer skills or exposed to high stress, are more likely to invest in retirement accounts regardless of the presence of the communist heritage. Specific influences (personality traits and life experiences) also resulted despite the increasing globalization, which, in the case of people over a certain age, was not able to erase some behavioral differences reflected until today.

In the Chinese stock market, the unique special treatment (ST) warning mechanism can signal financial distress for listed companies. In existing studies, classification model has been developed to differentiate the two general listing states. However, this classification model cannot explain the internal changes of each listing state. Considering that the requirement of the withdrawal of ST in the mechanism is relatively loose, we propose a new segmentation approach for Chinese listed companies, which are divided into negative companies and positive companies according to the number of times being labeled ST. Under the framework of data mining, we use financial indicators, non-financial indicators, and time series to build a financial distress prediction model of distinguishing the long-term development of different Chinese listed companies. Through data segmentation, we find that the negative samples have a huge destructive interference on the prediction effect of the total sample. On the contrary, positive companies improve the prediction accuracy in all aspects and the optimal feature set is also different from all companies. The main contribution of the paper is to analyze the internal impact of the deterioration of financial distress prediction in time series and construct an optimization model for positive companies. First published online 04 November 2021

The automation of document processing has recently gained attention owing to its great potential to reduce manual work. Any improvement in information extraction systems or reduction in their error rates aids companies working with business documents because lowering reliance on cost-heavy and error-prone human work significantly improves the revenue. Neural networks have been applied to this area before, but they have been trained only on relatively small datasets with hundreds of documents so far. To successfully explore deep learning techniques and improve information extraction, we compiled a dataset with more than 25,000 documents. We expand on our previous work in which we proved that convolutions, graph convolutions, and self-attention can work together and exploit all the information within a structured document. Taking the fully trainable method one step further, we now design and examine various approaches to using Siamese networks, concepts of similarity, one-shot learning, and context/memory awareness. The aim is to improve micro F 1 of per-word classification in the huge real-world document dataset. The results verify that trainable access to a similar (yet still different) page, together with its already known target information, improves the information extraction. The experiments confirm that all proposed architecture parts (Siamese networks, employing class information, query-answer attention module and skip connections to a similar page) are all required to beat the previous results. The best model yields an 8.25% gain in the F 1 score over the previous state-of-the-art results. Qualitative analysis verifies that the new model performs better for all target classes. Additionally, multiple structural observations about the causes of the underperformance of some architectures are revealed, since all the techniques used in this work are not problem-specific and can be generalized for other tasks and contexts. [ABSTRACT FROM AUTHOR]

Background: This paper describes the impact evaluation of a large big data platform initiative that is being undertaken in order to increase the probability of its success. The initiative, MIDAS (Meaningful Integration of Data Analytics and Services), is a European health-based Horizon 2020 project comprising a consortium of members from various universities, research institutions, and government agencies.

The present paper aims to present the main theoretical and practical aspects of what ontology and knowledge bases mean, having the role of providing as precise information as possible. Ontologies are considered to be a vocabulary of concepts, and the purpose of this "vocabulary" is to ensure a semantically based level of knowledge based on semantics in order to define semantic ideas and relationships between them. Starting from this concept, it should be noted the idea of using the knowledge by a system that groups them into a knowledge base, which means representing the experience accumulated by human specialists in solving the problems in the field in question. This knowledge refers strictly to an ontology of the domain, to the rules used, but also to the restrictions that minimize the search space.

Given the high volume of web information, more attention has been paid to the automatic data extraction systems. One of the most important methods of data extraction is clustering. Today, many clustering methods are provided which are mostly based on vector models. In these models, each document is treated like a set of words, and the sequence of words in the sentence is ignored. Since the meanings in the natural language are completely dependent on the sequence of words, a great deal of shortcomings is observed in these methods. To overcome these shortcomings, this paper presents a new method for clustering HTML documents in which STC algorithm is considered for clustering snippets. This method, called clustering based on KS_STC key sentences, provides a weighted vector for each document and using this vector, the key sentences of each text are extracted from the document. Finally, these key sentences are given for clustering to the STC algorithm.

Credit risk interprets as the probability of obligations non-repayment by customer in due date is considered as one of causes financial institutions bankruptcy. For this purpose, data mining techniques such as neural networks, Decision Tree, Bayesian networks, Support Vector Machine is used for customer segmentation to high-risk and low-risk groups. In this paper, we present the hybrid Imperialist Competitive optimization algorithm and neural network for increasing classification accuracy in evaluation and measurement credit risk of bank customers. The proposed method identifies the optimistic features and eliminates unnecessary features decreases problem dimension and increases classification accuracy. To validate this method, it implements on UCI dataset and also on a reality dataset of a private Iranian bank. The experimental results show this method is more satisfactory than other data mining techniques. The neural network error for the test set decreases with selection of effective features and elimination of low-impact features by the Binary Imperialist Competitive Optimization Algorithm. In addition test data error rate remains at acceptable level for other used classification methods. This article is the first use of algorithms Imperialist Competitive for credit risk assessment of bank customers.

In pharmaceutical industries, marketing and sales managers face many sales data from distributor companies. One of the methods that might help them to control the market, competing with rivals, devising the best plan to increase the sales and finally organizing the business activities, is to know about possible segmentations of costumers. The main purpose of this paper is to help the pharmaceutical industry marketing and sales managers, by the way of setting and analyzing different segments of costumers and to give proper suggestions that are appropriate for each segment, in order to maintain and increase the purchase trend using data mining methods. In this research based on regency, frequency, monetary and the purchase duration variables in RFML model, pharmacies are divided into different clusters and then analyzed. As a result of this segmentation, three categories of pharmacies will be recognized: premium, normal and cold and accordingly, the related analysis is provided.

To carry out their various missions—collecting revenue, facilitating trade and ensuring security— many customs administrations have established a risk management unit. In developing countries, however, because of the lack of dedicated human and material resources, intelligence and risk analysis remain insufficiently developed. In view of the lack of resources, this article proposes a simple methodology aiming at detecting risky import operations. The mirror analysis first helps to identify and target products or sectors with the greatest risk. Based on the examination of customs declarations patterns (data mining), it is then possible to identify and target higher risk economic operators (importers and customs brokers). When implemented in Madagascar, this method has helped to reveal probable fraud cases in the context of current customs reforms. [ABSTRACT FROM AUTHOR]

Business Management Education in India has shown an upward growth trend in the last couple of decades. Due to the diverse nature of the course, students from diverse academic backgrounds are being admitted to the course. Therefore, differences in students’ abilities and their learning styles have a significant effect on their learning outcomes. Meanwhile, with the development of learning technologies, learners can be provided a more effective learning environment to optimise their learning. The purpose of this study was to develop a model to automatically detect the students’ learning styles from their personal, academic and social media data and make recommendations for students, teachers, educators and administrators for overall improvement of learning outcomes. Data analysis in this research was represented using data collected from post-graduate business management students in India. A 10-fold cross-validation was used to create and test the models. The data were analysed by R and R-Studio. Classification accuracy, Precision, Recall, Kappa, ROC curve and F measure were observed. The results showed that the accuracy of classification by the C4.5 technique had the highest value at 95.7%, and it could be applied to develop Felder-Silverman’s learning style while taking into consideration students’ academic, personal information and social media preferences. [ABSTRACT FROM AUTHOR]

Given a large transactional database, correlation computing/association analysis aims at efficiently finding strongly correlated items. For traditional association analysis, relationships among variables are usually measured at a global level. In this study, we investigate confounding factors that can help to capture abnormal correlation behaviors at a local level. Indeed, many real-world phenomena are localized to specific markets or subpopulations. Such local relationships may not be visible or may be miscalculated when collectively analyzing the entire data. In particular, confounding effects that change the direction of correlation are a most severe problem because the global correlations alone leads to errant conclusions. To this end, we propose CONFOUND, an efficient algorithm to identify paradoxical correlation patterns (i.e., where controlling for a third item changes the direction of association for strongly correlated pairs) using effective pruning strategies. Moreover, we also provide an enhanced version of this algorithm, called CONFOUND+, which substantially speeds up the confounder search step. Finally, experimental results showed that our proposed CONFOUND and CONFOUND+ algorithms can effectively identify confounders and the computational performance is orders of magnitude faster than benchmark methods. [ABSTRACT FROM AUTHOR]

Recommender systems play an important role in our day-to-day life. A recommender system automatically suggests an item to a user that he/she might be interested in. Small-scale datasets are used to provide recommendations based on location, but in real time, the volume of data is large. We have selected Foursquare dataset to study the need for big data in recommendation systems for location-based social network (LBSN). A few quality parameters like parallel processing and multimodal interface have been selected to study the need for big data in recommender systems. This paper provides a study and analysis of quality parameters of recommendation systems for LBSN with big data.

Today the use of modern technologies in the daily life for satisfying the needs is unavoidable. Follow the news and searching through the internet has affected organizations to provide platform on the Internet for availability of information for the customers. With the development of e-commerce, online shopping plays an increasingly important role in people’s life. With the use of data mining technique prospect, managers of this site can analyze preferences and purchasing patterns of online customers in order to custom product recommendations. Data mining helps to provide services in accordance with customers’ requirements. The aim of this research is to identify the customers’ requirements in online shopping and cluster these customers based on independent attributes such as gender, product classification, recency, frequency and monetary. For this purpose, the data related to Nyazco website that is an e-commerce website with a variety of products, were examined as a case study in the period of 7 months. The authors of this paper will define four clusters by using k-means algorithm and RFM model by IBM SPSS Modeler 14.2 software. Customers in the third cluster and fourth cluster will be identified as the most important customers. Therefore, providing the demands of these customers should be prioritized.

The aim of this paper is to do a research of human resource management in Romania for the ITsector, and focus the attention to a couple of things like culture, trainings and the impact resultedon social media that this companies and their employees are producing. The use of social media has a huge impact on the quality of the work and also is contributing tostrengthen the relationships between employees. It can be a good resource in attracting new talentsand also promoting the company. This research used mined data from LinkedIn and other socialmedia and publicly available websites in order to statistically test hypotheses using the Pearsonchi-square method and successfully finding 6 strong correlations between data analyzed forRomanian software development companies.