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Sentiment analysis has been considered as a vital method to analyze a huge amount of documents in digital forms that are widespread and continuously increasing. In general, sentiment analysis plays an important role in social media analysis and business analysis. This paper illustrates sentiment analysis from machine learning methods (TF-IDF, Naive Bayes, SVM) to deep learning methods (Embedding, LSTM). We explain how the technique developed from sentence level to document level and analyze their advantages and disadvantages.

Managing organisational change is becoming increasingly important in an ever-changing world. A novel “lightweight” enterprise modelling technique called Fractal Enterprise Model (FEM) can be helpful in this effort. This technique is formally established, but the range of application areas for it is not yet fully explored. This paper aims to fill the gap, at least partially. This goal is being achieved by using FEM in an industrial project in which a water utility company wants to digitalise its business operations and external communication. The paper follows the Action Design Research paradigm, which combines theory generation with solving organisational problems. The project was structured along a generic Business Analysis process and contained elements from a number of fields, such as requirements engineering, information systems engineering, enterprise architecture management, and business process management. The paper describes 11 different generic problems that were solved with the help of FEM, the project-specific constraints and how FEM was applied in the process. As the same person carried out both the research and the practical work, the usefulness of FEM is examined via reflecting on the experience and generalising the knowledge. FEM supported the business analysis activities by enabling quick and systematic information gathering and comprehensible communication with various internal stakeholders. These insights can be used as a guideline for future practitioners planning to use FEM and for conducting further research regarding the applicability of FEM.

E-learning is used as one of the innovations in online learning, but in its implementation e-learning has limitations, especially in terms of flexibility, so that new innovations emerge, namely learning using m-learning because it is more flexible and can be accessed anywhere and anytime. The purpose of writing this study is to analyse problems and business opportunities related to the design of a mobile app-based start-up business in an e-learning system in a company and to design an information system in the form of an Android-based application to answer the analysis of existing problems and opportunities. The methodology used in this study is Rational Unified Process (RUP) with an Object-Oriented Analysis and Design (OOAD) approach which is described by the notation of the Unified Modelling Language (UML). The business analysis used in the writing of this study is the Business Model Canvas (BMC), Marketing Mix, Porter's Five Forces which is complemented by Financial Analysis using the Payback Period (PP), Break Even Point (BEP), and Return on Investment (ROI). In this case, Cleva creates an application system that helps companies carry out training for their employees and helps employees to learn about technical skills and soft skills through mobile-based applications

The purpose of this research to analyze problems and business opportunities related to mobile app-based design in food or beverage pre-ordering systems at places to eat or drink around the office or university and to design information systems s to answer problem analysis and the opportunities that exist. The methodology used in writing this thesis is the System Development Life Cycle (SDLC) with the Object-Oriented Analysis and Design (OOAD) approach illustrated through the Unified Modeling Language (UML) notation. The business analysis used in writing this thesis is the Business Model Canvas (BMC), Marketing Mix, Porter's Five Forces Model and SWOT analysis that is equipped with Financial Statement Projection. This application system that helps users to pre-order food/drinks online through a mobile application by collecting together places to eat/drink around offices and universities (canteen, food court, and restaurants), and helping partners in writing orders and making financial statements and buyer behavior.

The article considers the development of IT students' professional competencies and skills of modeling and reengineering of business processes on the example of training bachelors in the specialty 38.03.05 “Business Informatics” at NUST MISIS. The key problems of IT specialists training (especially, the issues of the development of professional competencies) and methods of their solution are considered. A sequence of modeling steps has been proposed, enabling one to perform a system and business analysis of an enterprise as an economic and management entity for the purpose of its business processes reengineering, up to the implementation of a prototype of the process under study with respect to its robotization as part of the graduation thesis. The article summarizes the experience of using the competence-activity approach, interdisciplinary communications and blended learning using an electronic educational environment and adaptive educational trajectories, including during the pandemic.

In the fast-changing Internet era, the advantages of e-commerce over traditional shopping models are becoming more and more obvious, and convenient and fast online shopping patterns are attracting more and more users. At the same time, large-scale transactions and demand between e-commerce competition is becoming increasingly fierce, inter-enterprise competition on the one hand to promote the development of e-commerce, at the same time, but also accelerate the survival of e-commerce. Enterprise competition has intensified, customers to the enterprise, has become the most important resource, how to attract customers and retain customers has become the focus of the enterprise, which also makes customer loss become the concern of many enterprises. E-commerce companies in order to ensure their own healthy development in the fierce competition market, not only to make their products attractive, but also in-depth understanding of user preferences and satisfaction, the user’s behavior characteristics of in-depth exploration. E-commerce user behavior instability is greater, the churn rate is high, then, can we find customers in time before the loss, while helping the marketing department to target the loss of customer base and develop appropriate marketing programs is an important work of the enterprise marketing department.It is an important work in the daily operation and management of e-commerce enterprises to predict the loss of users more accurately, to implement targeted retention strategies for users who are at greater risk of loss, and to reduce the churn rate. In these areas, data mining can help businesses. In this paper, data mining technology is applied to business analysis to predict the loss of Tmall users within a certain period of time, so as to implement retention strategy and reduce the churn rate.

This paper mainly focuses on the design and implementation of the pet community platform system based on big data. Pet community platform system is divided into two subsystems: client and administrator. The two subsystems are developed in the way of front and rear end separation. The back end uses the current mainstream JavaWeb development framework SpringBoot+ MyBatisPlus, and the front end uses the popular VUE +Element UI framework. The database of the system uses MySQL8 +Redis, and the platform system uses NGINX to achieve load balancing. After system requirements, feasibility analysis, business analysis, case analysis, system function of activity diagram and the database design and detailed design, using the MVC model view controller design specification, design to achieve the system each function module, through the way of writing test cases and manual test to test the main functions of the system, the system of the client subsystem mainly for the majority of raising pet pet professionals provide a communication platform with pet as the theme of community, published on the platform users can questions about pets or subject, the platform can communicate the case for users. This system use enterprise Java applications for pet lovers create pet exchange platform for the community, the community will gather all kinds of pet owners, users can learn all kinds of pets here knowledge and skills, to communicate with other pet owners to discuss, share moments of pet, pet adoption information release, we work together to solve the problem of pet relocation. At the same time, it provides data collection and analysis to provide strong data support for the development of pet market.

The only way to provide feedback about a product is through reviews. When a new shopper proceeds to an online shop to purchase a product but does not have adequate time to study the reviews provided by other shoppers to get an opinion about the product, the shopper determines whether to buy the product or not on the number rating. Through reviews, shoppers can acquaint everyone about the product's quality and the manufacturer can enhance their products and business by interpreting that review. However, manufacturers demand a number review more than a text review for business analysis. This paper represents a machine learning based model for predicting the number rating from written text for Bangla product review. This study performs on a dataset collected manually from Daraz.com.bd, a Bangladeshi leading e-commerce shop. We have implemented Support Vector Machine (SVM), Random Forest, XGBoost, and Logistic Regression with Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer on our collected dataset and record all the performance metrics like accuracy, precision, recall and f1-score. From these above four algorithms, SVM showed more outstanding results than others in terms of performance metrics. SVM achieved 90% accuracy on the applied dataset. The other SVM performance metrics are 0.90, 0.92, and 0.91 for precision, recall and f1-score respectively.

In order to make the teaching resources fair, many cities now implement the allocation mechanism. But in the process of allocation, government staff needs to manually review a lot of work, which increased a lot of pressure to the education management department and wastes a lot of manpower and resources. Moreover, because there are persons take part in the allocation management, the public doesn’t have full trust in the allocation. In order to reduce the work pressure of government departments and improve the trust, the government should use blockchain technology to realize management system. By analyzing the business process of student allocation system, the system can store key data with alliance chain technology. Alliance chain breaks through the data barrier of information system among government departments. According to the characteristics of the alliance chain, this paper gave the overall design and business process analysis of the system, and the system experiment finally verified that the blockchain technology has very important significance and feasibility in the sunshine government. The next research work will be to design the application system by using the block chain principle, instead of using the existing block framework.

Sentiment analysis is a widely used topic in Natural Language Processing that allows identifying the opinions or sentiments from a given text. Social media is the scope for the customers to share their opinion over the products or services as part of customer reviews. Dissect this review has become an important factor for business analysis since online business is exponentially growing in today’s techno-friendly competitive market. A large number of algorithms have been found in recent articles. Among those deep learning is an important approach. In the proposed methodology, long short-term memory (LSTM) and Gated recurrent units (GRUs) have been used to train the hotel review data where the accuracy rate of identifying customer opinion is 86%, and 84% respectively. The dataset is also tested by using Naive Bayes, Decision Tree, Random Forest, and SVM. For Naive Bayes obtains an accuracy of 75%, for Decision Tree obtains an accuracy of 71%, for Random Forest the accuracy is 82% and for SVM our accuracy result is 71%. Deep learning is used to obtain better business performance and also get the review from customers and also to predict the sentiment about customer review. Our algorithm works properly and gives better accuracy.

An online product's rating is an essential metric to understand the acceptability of that product to users. Shoppers use the rating to measure the quality and excellence of the online product. It helps an online shopper to decide to buy a product or not. It also helps the producer to further modification of that product during reproduction. Sometimes people buy a product online and give a text review of buying products but apathy towards giving a number rating, commonly a star rating. But producers need to know the rating of products for analysis of their business. Producers can use this rating for business analysis and can drive better revenue to their business. We have used some supervised machine learning approach to predict rating based on customer text review and compared the results between Random Forest Classifier, XGBoost and Logistic Regression algorithm with TF-IDF Vectorizer from extensive and series of experiments. We applied the algorithms mentioned above on the dataset named “GrammarandProductReviews” provided by Datafiniti. We analyzed the performance of each algorithm through the accuracy, precision, recall and f1-score. From the study, it is observed that Random Forest algorithm gained accuracy of 94% and precision, recall and f1-scores are 0.94, 0.94, and 0.94 respectively, which showed best compared to others.

There exists a lot of valuable information with decision-making significance hidden behind the historical data. The methods of statistics and artificial prediction has been unable to well mine the useful information of telecom business decision-making. Therefore, this paper adopts MSSQL, .NET, Ajax and other related technical theories to complete the realization of the revenue forecast system. Firstly, the difficulties and challenges faced by telecom enterprises are analyzed, and the necessity of establishing business analysis system based on data warehouse is put forward. Then, based on in-depth study of data warehouse and business analysis system technology, the data warehouse model of enterprise business analysis system is analyzed in detail, and the grey prediction data of trained weight and threshold network is performed. Finally, through the comparison between the prediction results and the actual data, which model has the advantages of low cost is proposed. The selected model has higher accuracy. The application results show that the prediction accuracy of the combined forecasting model is higher than that of the previous methods, and the prediction results are more stable.

5G networks will provide services for a wealth of vertical industry applications, bringing more demand for edge services. Multi-access edge computing (MEC) is one of the important technologies of 5G. By providing an information technology service environment and cloud computing capabilities close to the mobile users, it can better support low-latency and high-bandwidth services in 5G networks. This paper constructs an AI capability center based on 5G MEC, which can provide rich AI services for smart grids. The center supports the dynamic construction and operation of the intelligent data analysis application, and realizes the rapid development and flexible deployment of the business analysis process, which could improve the interaction and response time of the business analysis process. Based on AI algorithm capabilities, we focus on the typical cases of artificial intelligence service applications in smart grid e.g., monitoring of electrical equipment based on similar audio recognition. We describe the applications of artificial intelligence technology in various business scenarios from the perspective of application scenario, application scheme, algorithm principle and practice effect. The applications have been put in use and obtained good application effect.

In today’s digital world, advancement in machine learning has changed the traditional perspective towards business analysis. Orthodox business analysts did not consider customer reviews as cost-effective input for analysis because earlier fetching customer reviews was costly. The emergence of the internet turned the whole world upside down. Now, the customer’s sentiment analysis is the new friend of all business analysts. In this paper, we will be doing a comparative study between the two methods. One in which we use usual parameters like average rating, average review counts. In second, we use a text review as a parameter for sentiment classification. The effectiveness of these methods is evaluated, and the optimal model is selected.

With the continuous emergence of large companies, businesses in different domains have been amalgamated, which leads to a prevalence of crossover services. Different from service choreographies and collaborations, the crossover services involve multiple domains and are highly integrated in terms of processes, entities, abilities, and resources. In a crossover service, the elements of independent service are disrupted and reorganized, which leads to the increase of the complexity of traditional modeling methods. This new form of service brings challenges to business analysis and modeling, such as semantic ambiguity, business coupling, and test explosion. In this work, a crossover service-oriented modeling method, namely CoSM, is proposed to help with the construction, reuse, and analysis of crossover services. CoSM is designed based on scenarios, which can be used to slice the processes and route to each other through the triggers. Besides, a prototype platform is built and concepts and processes can be constructed through that. We verify CoSM in a real case and design experiments to compare with pertinent models. Experimental results show that our model can express the same process with fewer connectors.

The presence of e-commerce support SMEs to promote and market their products online be able to compete on a global scale. However, the perpetrators of SMEs still do conventional promotion and marketing. The use of e-commerce technology is low as a media promotion and market place result SME has not been able to reach a global scale and profit only get by a conventional sell. To facilitate SMEs in order to promote and market products online, so it needs to be made a prototype e-commerce portal as a place for the perpetrators of SMEs. Business process analysis does by functional analysis method. Analysis of functional then designed a business development model based on business model innovation by adopting SMEs. Designing a prototype using UML, then do the testing of User Acceptance Test (UAT) for the prototype that all respondents agreed that the e-commerce portal prototype has been built in accordance with the requirement of the end-user and the quality of the prototypes had good criteria with a percentage of 83.86%. The result of this research is a prototype e-commerce portal as a platform for SMEs to promote and market their products and make it easier to get information for the advancement of SMEs in the form of news from relevant agencies distributed in the e-commerce portal.

With the rapid development of internet technology, informatization and digitalization have penetrated into every link of human social life. A large amount of sensitive data has been accumulated and is still being generated within the enterprise. These sensitive data runs through the daily operation of enterprises and is widely used in business analysis, development and testing, and even some outsourcing business scenarios, which are increasing the possibility of sensitive data leakage and tampering. In fact, due to the improper use of data and the lack of protective measures and other reasons, data leakage events have happened again and again. Therefore, by introducing the concept of fuzzy set and using the membership function method, this paper proposes a desensitization technology framework for industrial data and a data desensitization algorithm based on fuzzy set, and verifies the desensitization effect and protective action on sensitive data of this algorithm through the test data desensitization experiment.

Business analysts are just as indispensable as they always have been, but now we need to work differently, in a more agile way, to fit with agile development. This talk shows you how to do that. It is about three things:•Too many projects start with an assumed solution, often by recording this assumed solution as a backlog. This talk shows you a better way to start.•Spend enough time with the customers to understand their work, their desired outcome, and what it is they value. We talk about generating alternative candidate solutions and testing them using quick, safe-to-fail probes. The justification for multiple candidates is to get away from the assumed solution, and validate the customer’s problem space by exploring different ways to solve it.•Use a story map to organise the stories written by the business analysts. The BAs write the stories in units corresponding to business events. The product owner prioritises the story map (which is in effect prioritising the business events) and thereby synchronises the work of the business analysts with that of the developers.

Business analysis is using large volume of data to help people to make decision. Supervised learning methods are widely used in bank system, especially in credit card approval. The goal is to use various data mining algorithms to predict whether a new customer credit scores or to detect credit fraud. This will serve as the basis for the design of a new campaign because a transition from manual review to automatic review by computer could dramatically improve the work efficiency and reduce the labor costs. More tools such as self organizing map and algorithms could shed light on the self-evaluation for credit card applicants, which could raise efficiency.

The Greek Integrated Information Citizenship System “Gr.I.I.C.S” (pronounced “Greeks”) is used by the competent Services nationwide. The Central and Regional Citizenship Directorates of Greece process thousands of cases of varying levels of complexity every year and it has been apparent that an expert system should be created to aid them in this task. In 2017, “Gr.I.I.C.S” was put in productive use that modernized procedures. In this work, we dive into the processes of the complex Citizenship Law provisions. The business process analysis is focused on the evaluation of two popular citizen service scenarios. Our work shows that proposed approaches will shorten the service time needed for each case and it contributes at the same time to a more effective management of citizenship employees.

The long-term development experience of undergraduate (BSc) and graduate (MSc) curriculum for the Business Information Technology study programs at Tallinn University of Technology (TalTech) are presented and analysed in this paper. The three-year undergraduate program aims to prepare junior specialists for organisations and businesses who have the theoretical knowledge and practical skills for planning, development and maintenance of enterprise information systems. In the two-year graduate program, the target is to educate leading and senior specialists in the fields of (a) business analysis and enterprise architecture, (b) information systems analysis and architecture, and (c) data analysis and business intelligence. We analyse the development of the Business Information Technology curriculum at TalTech over the last ten years, evaluate our experiences and present the resulting curriculum together with their underlying unified curriculum model. The main contribution of this paper is to exemplify and analyse the use of modern teaching theories in curriculum development practice.

Robotics Process Automation is an advanced technology that builds an intelligent software robot that can emulate human interactions with a business process. RPA is an efficient automated method where software agents interact through a graphical user interface in a human-like manner. RPA has various applications in most industries like banking and finance, human resources, healthcare, etc. In this paper, we have proposed our RPA process analysis model and have compared it with a traditional model using various comparison parameters like frequency of change, degree of complexity, time is taken, screen usage and volume of transactions and found out that proposed method gave efficient results over the traditional method.

The development of information technology is currently influencing the organization; one of them is Telkom University. Online-Based information technology at Telkom University, namely the Integrated Academic Information System (I-Gracias). I-Gracias is an Academic Information System owned by Telkom University that provides services both academically and non-academically. One of the menus in i-Gracias is the TA / PA application; this menu intends for students who will take the bachelor degree final project and will carry out the bachelor degree final project. Based on the current procedure, the service standards for the TA / PA I-Gracias application provided do not have specific service time limits. There are differences in service for each user in the TA / PA application activity. Therefore, to find out the actual operations in this application, a modeling process is needed to find out the activities that have the longest time. Identification of the modeling process is made by using the process mining approach and utilizing the I-Gracias log event. The modeling process uses the Heuristic Miner modeling technique aimed at modeling the process and finding the best fitness value. The heuristic miner algorithm is chosen because of its ability to handle event logs with noise and can display primary behavior from existing business processes. After getting the modeling, then analyzing the bottleneck for the procedure. Based on the conformance checking results, the best fitness value is 0.98492914, the precision value is 0.7015873, and the structure value is 1. Modeling in performance analysis shows that there are bottlenecks in the activity of uploading proposals and completeness of print trial activities. Bottleneck shows the problem related to the bachelor degree final project guidance application.

China aims to lift out all the impoverished people living below the current poverty line out of poverty by 2020, this is still a great challenge. Big data technology is used for targeted poverty alleviation. Identification of the poor households is based on the data from various channels. This paper introduced big data and standardization researches and applications in targeted poverty alleviation and presented a standard system framework of targeted poverty alleviation under the background of big data, and the methodology, business analysis of poverty alleviation are also presented.

Technological changes and globalisation have dramatically changed the way personal data are processed. It takes time to understand the legal bases for data processing, auditing and confidentiality rules, and it cannot be easily verified that people's personal data are processed legally. Using a model for compliance with GDPR requirements turns data controllers from mere consumers of consultancy services into managers of the privacy protection system. ISO 27552 is the upgrade which gives the information security system a completely new status, transforming it into a privacy protection system.

This paper presents a suitability evaluation approach to assess whether the LTE-based wireless private network satisfies the communication needs of smart grid applications. This approach can obtain different evaluation indexes including reliability, delay and throughput when the business attributes are used as the initial inputs by analyzing the MAC and PHY layer models and coverage characteristics. Then combined with the business analysis model, the applicability of wireless private network can be evaluated according to different power business needs. In order to verify the use of the proposed evaluation method, a simulation analysis of the suitability of the LTE 1.8GHz wireless private network is conducted for the businesses of power distribution and utilization.

Maritime transportation or sea freight is the major conduit of international trade which transported with container. In 2017, the volume of import and export seaborne trade in Thailand achieved 143.7 and 72.2 million tons, respectively. Moreover, the number of containers used were 3.7 and 3.9 million TEU for import and export by international cargo vessels, respectively. Due to high competition and container usage in this sector, shipping agent who is a representative of the owner of the ship and container try to beat the competitors by maximizing the customer satisfactions. The key success is the container service fulfillment by sorting the suitable containers located in a container depot which is the area for placing containers for a shipping agent. Currently, the main problem found in the container depot is long empty container receiving time and container inventory controlling due to inefficient management. In this research, we investigate the business processes in the container depot by using IDEF0 level 0 and level 1 and analyze the problems found in each process and propose the recommendation in order to increase supply chain efficiency and improve business service.

Business analysis is performed to determine the business that are popular, in Indonesia with text mining can take data from several news portal in Indonesia. Text preprocessing is used to change the text title and tags on the news to be converted into weights. The weight of the data will be processed using the K-Means algorithm to be grouped into clusters and each cluster will be visualized using Word Cloud so that words that often appear as popular word identification are known. Testing uses the Silhouette Coefficient to calculate the quality of each member against the cluster. Furthermore, each member will be interpreted according to the test results. Analysis is carried out every month in 2018 with a total of 995 data with a monthly average of 6 clusters, in January were the most popular business according to the number of members from 64 data formed 6 clusters, the most member clusters were cluster 1 the Silhouette Coefficient test results are strong 0.00%, medium 65.22%, weak 30.43%, not substantial 4.35%, Word Cloud formed was a leather bag business.

Visualizing operational log data is very effective for understanding the actual state of complex operational processes and for supporting decision-making of users in actual operational fields who understand the operations. However, non-expert users in analysis and/or visualization have difficulty in finding parts to be drilled down in accordance with operational purposes from a large number of operational log data. This paper proposes a heatmap-based visualization technique to overview potential problematic parts from a large number of operational log data by emphasizing the important parts calculated by combining multiple indicators in accordance with analytical purposes. To verify the effectiveness of the heatmap-based visualization, this paper introduces visualization results of applying a prototype tool using the proposed technique to operational log data of actual operations.

Businesses and governments globally are transforming their operations to capitalize on emerging ICTs for higher quality services. Businesses often evaluate emerging ICTs as a strategic medium to expand their products and services globally at a reasonably lower cost. Recently smart devices and connectivity are within reach for many people. Eservices is a general term for services offered to customers via the internet and other ICTs. Eservices extends to the sector of governance commonly referred to as public eService spread between different municipalities across SA. Development of public eService systems tend to be complex and rife with challenges. Efficient public facing systems require consideration of a wider range of Business Analysis (BA) aspects when compared to more well defined systems used in the private sector. Despite adherence of well accepted tools, methods and standards from the BA domain, challenges still persist. This study proposes a theoretical BA framework specifically to address the unique needs of creating public eService systems. Following a literature review, thematic analysis aided in the discovery of critical components of the proposed BA framework. The themes derived will assist with categorisation which will conceptualise each component and ensemble the framework.

The main enabler to Smart City paradigm is a robust and efficient power distribution system, which requires that this infrastructure need to be inspected regularly, making that maintenance paradigm change from reactive to predictive. This paper presents the business analysis process and its result – the SIAD-AERO Project. SIAD-AERO is a UAS composed of a set of different aerial platforms, decision-making system; command and control stations and an advanced artificial intelligence system that provides a complete toolbox to perform an autonomous inspection and detection anomalous. At the end of this paper, the result of impact business analysis is presented, as well as the description of the anomalous pattern recognition process used to perform the anomalies automatic detection for the power line inspections.

Quick response times, ad-hoc queries, and reduction the load on existing productive systems are, among others, the most important reasons for using enterprise data warehouse systems (DWH). The fixed-structured multi-dimensional data model such as Star schema allows OLAP functionalities to be pre-programmed and executed by an OLAP engine without the need to any programming activities in runtime. In opposite, structures of business processes are dynamic. Business process analysts focus on the analysis of changing of processes over the time and their behavior. Therefore, it is not sufficient using traditional OLAP Engine of DWH just to investigate information about aggregates key performance indicators related to business processes like total execution time or process cost. This paper presents a conceptual generic data model for matching business data of business objects in a standardized business processes.

Requirements Engineering and Business Analysis are known as very important to software project outcome but also difficult activities., coping with many problems and challenges. The work reported in this paper was preceded by a survey which revealed most common requirements-related problems in Polish IT industry. We addressed ten most frequently reported problems by reviewing the literature for recommendations how to cope with those problems. The resulting set of recommendations is included in the paper. Next., we conducted interviews with three experienced IT analysts asking them to assess effectiveness of particular recommendations., based on their experience. The results show significant differences in assessments and indicate that effectiveness is dependent on contextual factors to a large extent. Our conclusion is that a follow-up work is required to document more recommendations and to annotate them with guidelines about applicability., intended context of use and possible pitfalls.

The increasing demand for data mining in business intelligence has led to a significant growth in the adoption of data mining as a service paradigm which enables companies to outsource their data and mining tasks to a cloud service provider. Despite the popularity of the paradigm, the companies hesitate to enable the cloud providers' access to their data considering customer privacy and intellectual property. In this paper, we propose a privacy-preserving two-party protocol which aims to mine direct sequential patterns from outsourced protected data. We focus on direct sequential pattern mining since it is a widely used primitive in business process analysis. Considering the accuracy and confidentiality, we choose encryption over statistical methods for data protection and processing. To be able to process the encrypted data, we adopt a homomorphic encryption scheme, ElGamal cryptosystem. The novelty of our scheme is that it introduces an encryption switching method that enables us to use both multiplicative and additive homomorphism on ElGamal cryptosystem. The results of our analyses show that our protocol is more efficient than the state-of-the-art proposals in terms of computational cost with a similar communication cost.

Data mining is a methodical process of discovering data patterns and models in large data sets that involve methods at the intersection of the database system. This paper issues the popular problem of the extraction of high utility element sets (HUI) in the context of data mining. The problem of these HUIs (set of elements of high usage and value) is mainly the annoying mixture of frequent elements. Another addressable issue is the one of pattern mining which is a widespread problem in data mining, which involves searching for frequent patterns in transaction databases. Solve the problem of the set of high utility elements (HUI) requires some particular data and the state of the art of the algorithms. To store the HUI (set of high utility elements) many popular algorithms have been proposed for this problem, such as “Apriori”, FP growth, etc., but now the most popular TKO algorithms (extraction of utility element sets) K in one phase) and TKU (extraction of elements sets Top-K Utility) here TKO is Top K in one phase and TKU is Top K in utility. In this paper, all the aforementioned issues have been addressed by proposing a new framework to mine $k$ upper HUI where $k$ is the desired number of HUI to extract. Extraction of high utility element sets is not a very common practice. Although, it is indefinitely being used in our daily lives, e.g. Online Shopping, etc. It is part of the business analysis. The main area of interest of this paper is implementing a hybrid efficient Algorithm for Top K high utility itemsets. This paper implements the hybrid of TKU and TKO with improved performance parameters overcoming the drawbacks of each algorithm

BACKGROUND: In the paper, we will provide detailed research on hospital information systems integration to national health record system. The main problem discussed in the paper is the complex integration process of EHR. Business processes are too complex and too much coupled. The result of it - medical doctors spent too much time for creating needed documents for the patients, they make too many mistakes because they are lost in the process and every interrupt in national health record system affects their work process. Finally, because of the many tightly integrated components, the system is not as reliable as expected. OBJECTIVE: The main goal of the paper is to describe effective business process-based architecture of hospital information system integration to national electronic health record system. METHODS: Scientific business process analysis of health care information systems were made and new EHR integration architecture were proposed and described. Number of daily statuses were collected from event logs recorded between September 1, 2017 and February 1, 2018. Data were divided into 2 week time intervals and number of daily statuses in time intervals were compared with the Kruskal-Wallis test. Data were presented as mean and 95% confidence intervals. A P value of

In this paper, I present a computer tool specially designed to be applied for the statistical analysis of data collected in the business field, for quality control. Companies that use information technology have recorded data that, properly treated, give them an advantage over other companies. It has been demonstrated over time that the success of the business analysis depends on the quality of data and the availability of well-trained analysts who have a very good understanding of both information technology and business. An application to analyze the economic evolution of a company was designed and tested. The analytical and graphic approach of those features that highlight the various aspects relevant to conducting in optimal conditions the company activity have been considered in the design of the application. The programming language chosen for the application implementation was C#, part of Visual Studio. The experimental data set was stored in a database using Excel spreadsheets, form Microsoft Office.

With the social concerns about the safety issues of meat products in recent years, consumers' demands for green and healthy mutton increase rapidly. However, the confidences in meat products along with the weak construction of overall industrial integrity system have driven the consumers to make the choices unfavorable for the enterprise integrity management. However, the realistic characteristics of industrial chain, such as, in-depth vertical coverage, disperse horizontal distribution, changeable dynamics and complex factors influencing the quality and safety of supply chain make it difficult for enterprise to carry out uniform quality and informatization management. This paper analyzes the problems faced by enterprises in the running status of meat supply chain and the trend of industrial chain integration in China. The paper proposes to establish a Traceable System and Evaluation Mechanism of Mutton Quality by taking all industrial chains into consideration and based on the principle of full life circle, namely, from origin of production to ultimate consumers. Based on the business process analysis of mutton supply chain, health and epidemic prevention, food control and other aspects, the key control points of mutton supply chain were traced back.

With the recent remarkable and fast evolution in telecommunication and computing technologies, great amounts of individuals' tabular-formatted data are collected and used by several organizations in the society. In some cases, some organizations need to share these gathered data to be used in business analysis, decision making or scientific researches purposes, which can involve sensitive information about the individuals. However, these data cannot be published in their original form to other third parties due to the associated privacy concerns. Consequently, preserving individuals' privacy represents a critical issue when sharing the individuals' private data. Hence, Privacy-Preserving Tabular Data Publishing (PPTDP) has received a great attention to protect the privacy of individuals' tabular data, where several approaches have been presented to address this issue. In this paper, we propose an enhanced additive noise approach for privacy-preserving microdata with Single Sensitive Attribute (SSA) publishing. The proposed approach maintains better published data utility to allow more accurate mining and analytical results, where more robust privacy protection against privacy attacks is provided.

Sentiment Analysis is an approach of studying people's emotions and sentiments based on a particular topic, service, event and it attributes. Sentiment analysis appears as a part of various business analysis systems to find opinions about their services or products. Both the availability of CPU resources and enormous amount of data generated by the users makes sentiment analysis an active research field in upcoming years. Most of the existing approaches focus on efficient feature extraction, at the same time some approaches focus on extracting semantic features, which makes mush contribution to sentiment analysis. This review paper gives a comprehensive overview on Sentiment analysis using NLP (Natural Language Processing) techniques. First, we start with some NLP techniques which are generally needed for preprocessing the input data. Second, we introduce various approaches and some problems related to sentiment analysis and discuss some challenges and problem in sentiment analysis. Finally, we illustrate recent trend in sentiment analysis and its related works.

Fast-paced era development encourages organizations to undertake an adjustment. This kind of adjustment can be fulfilled by performing Strategic Management in which formulation, implementation, and evaluation for a set of decision-making to be performed to achieve specific objectives. Advanced market demand and rapid technology development are example of factors which encourage organizations to keep up with the current development. New business ideas or product concepts can be developed to help organizations dealing with that challenge. The implementation of new business ideas or product concepts can be performed by using Business Model Canvas and Technology Roadmap. Both tools provide different perspectives in the implementation of new business ideas or product concepts. However, when both tools are utilized together, organizations can obtain both perspectives using resources and time in a more efficient way. A study has been conducted to implement the building process integration for Business Model Canvas and Technology Roadmap. Set of analysis and a case study are performed to examine the methodology for the building process integration of Business Model Canvas and Technology Roadmap as implementation of strategic formulation phase in the context of Strategic Management, using Business Analysis tools. Business Analysis tools is utilized to identify internal and external factors for organizations in effort to achieve new business strategies.

This article discusses the application of Project Management methods in the development of a set of projects. The result of one of which will be the development of an effective methodology for formalizing a complex business process and on the basis of this methodology the development of models and methods for building and automating a single business process of the licensing process of the Republic of Kazakhstan. To date, conducted a systematic analysis of the business process in the system of governance and management of companies and the characteristics of complex business processes: the essence and content, as well as the essence of management of the company's business process. Methodologies for formalizing a complex business process based on specialized models are considered: strategic, conceptual, logical and procedural, each of which is designed to model certain aspects of the business process.

In this paper, we address the issues associated with setting-up of a new restaurant business. To strategize a new restaurant business, we propose a restaurant business framework which comprises of 3 most important tasks, namely, high frequency attributes, most crowded day and location of the restaurant. First, we identify the features/attributes of the restaurants in which the customers are most interested in and provide those facilities and services to increase the profit. Next, we identify the day of the week when the restaurants are heavily crowded so that the best recipes and offers are made available on those days. Finally, since location has a profound effect on the success of a restaurant business, we consider location to be the most important to know the nearby restaurants and their facilities before coming up with a new restaurant business. The performance analysis of the proposed framework was carried out on the standard Yelp dataset. Thus, we found credit card to be the most preferred attribute, the most crowded day to be Monday and Divey to be the most desired ambience among the customers. We also demonstrate how the new restaurant can be setup by identifying the nearest restaurants and the services.

Currently increasing in data exhaustive processing systems of information is now becomes very important for making decisions in the business or companies. Important and responsive knowledgeable designs may consist in process of the business analysis. Now a day privacy-preserving data-mining may obtain a huge enhancement with the help of data-mining and the researchers of the Information security community in the development of the technologies that are merges with the concerns of privacy. In the original data, the random-noise is added in the privacy preserving data Mining (PPDM) method, that is used for publishing the exact information regarding the actual data. The basic perspective of the privacy preserving data mining is to design various algorithms for updating the actual data and for providing the security to the information which is to be get misused, as a result of this the private data and private information still remain same even after the mining process. This paper calculates the results on comparative dependent analytical of the privacy preserving data mining algorithms and implemented those in MATLAB.

To better reorganize and optimize business process, Petri nets is a good formal tool. However, the prototype Petri nets can't fully describe the related properties of the business process. In this paper, the time and cost attributes are added to the Petri nets and it is called the extended generalized stochastic Petri nets. On this basis, the performance evaluation model is proposed. Execution efficiency and cost of Business process can be got by the evaluation model. They provide a direct basis for business process reengineering and optimization. Finally, the effectiveness of the model is validated through case study.