Vishal Bhatnagar

Role of knowledge management and analytical CRM in business: data mining based framework

Purpose – The purpose of the paper is to provide a thorough analysis of the concepts of business intelligence (BI), knowledge management (KM) and analytical CRM (aCRM) and to establish a framework for integrating all the three to each other. The paper also seeks to establish a KM and aCRM based framework using data mining (DM) techniques, which helps in the enterprise decision‐making. The objective is to share how KM and aCRM can be integrated into this seamless analytics framework to sustain excellence in decision making using effective data mining techniques and to explore how working on such aCRM system can be effective for enabling organizations delivering complete solutions.Design/methodology/approach – This paper is based on focused and dedicated study of the literature present on the aCRM, KM and data mining techniques. The paper considered how to develop a strategy and operational framework that would build aCRM on the foundation of existing DM techniques and KM approach to meet the business chall...

A holistic framework for mCRM – data mining perspective

Purpose – The purpose of this paper is twofold. First, in order to understand mobile customer relationship management (mCRM) and data mining application in the mCRM, this paper aims to present a conceptualization of mCRM in respect of data mining. Second, the paper also aims to develop the empirically grounded framework of the mCRM from data mining perspective.Design/methodology/approach – The empirical paper is used to gain a conceptual view of mCRM. Semi‐structured interviews and contact methodology is used to form the main data source through which the major concerns and issues of mCRM are identified. This lead to holistic framework of mCRM. The paper followed the paradigm of natural science research on information technology by March and Smith and Hervner et al.Findings – The framework identified three critical issues that are categorized as customer care information center, data store and data access systems, and mobile services and technology. The paper on various existing literatures in mCRM strate...

Concoction of Ambient Intelligence and Big Data for Better Patient Ministration Services

The term Ambient Intelligence (AmI) encompasses other technologies such as ubiquitous communication, pervasive computing and ubiquitous computing. Hospitals can improve their workingbymonitoringthehealthofthepatientsandperformingautomaticanalysisofvariousand healthparameters inside the room.Securitymechanismscanalsobeenhancedbyonlyallowing authorizedhospitalstaffandattendantsintheward.WiththeadventofAmbientIntelligenceand thecongenialpoliticalenvironment,thefocusisnowshiftingtoprovidingbetterhealthcareathomes thanattraditionalmedicalcenters.Inthispaper,weimplementedanalgorithminwhichweconsider aspecificroomofahospitalastheenvironment,withapatientmonitoredforhealthandsecurity reasons.Ifanythingisnotallowedfortheparticularpatientortherearesomeunwantedvariations inthehealthparametersofthepatient,thealarmwasrangandthepatient’sassistantswerenotified. KeywoRDS Ambient Intelligence, Big Data, Sensors, Ubiquitous Computing

Movie Analytics for Effective Recommendation System using Pig with Hadoop

Movies have been a great source of entertainment for the people ever since their inception in the late 18th century. The term movie is very broad and its definition contains language and genres such as drama, comedy, science fiction and action. The data about movies over the years is very vast and to analyze it, there is a need to break away from the traditional analytics techniques and adopt big data analytics. In this paper the authors have taken the data set on movies and analyzed it against various queries to uncover real nuggets from the dataset for effective recommendation system and ratings for the upcoming movies.

A framework for analytical CRM: a data mining perspective

The authors in this paper present customer relationship management (CRM) analytics and its framework from data mining (DM) perspective. The paper also presents the benefits to any enterprise that will reap the same by incorporating the framework. As all of us are aware, DM is the extraction of previously unknown and hidden information from the databases. Its tools can answer customer-related questions that were too time-consuming to resolve earlier and even in some case not possible to answer. They provide the hidden patterns, predictive information that experts may miss. Analytical CRM helps in better understanding of the customers by evaluating customer-related data using the tools like DM. Greenberg (2004) defined analytical CRM as the capture, storage, extraction, processing, interpretation and reporting of customer data to a user. The paper also justifies the framework with an empirical example.

Analogy of various DNA based security algorithms using cryptography and steganography

In todays era Information technology is growing day by day. The rate of information storage and transformation is growing day by day. So information security is becoming more important. Everyone wants to protect its information from attackers and hackers. To provide security to information there are various algorithms of traditional cryptography and steganography. New field DNA cryptography emerges to provide security to data store in DNA. The DNA cryptography uses the Bio-Molecular computational abilities of DNA. In this paper authors compare the various DNA cryptographic algorithms on certain key and important parameters. These parameters would also help the future researchers to design or improve the DNA storage techniques for secure data storage in more efficient and reliable manner. The authors also explain the different biological and arithmetic operators use in the DNA cryptographic algorithms.

Analytical customer relationship management in insurance industry using data mining: a case study of Indian insurance company

The insurance organisations have strong customer base with vast amount of data which is very difficult to manage in terms of finding unknown and hidden patterns from it. The growing demand of information which will provide assistance to decision makers in building a strong customer base and good image in customers mind is guiding a path towards extensive usage of analytical tools for revealing hidden information|s. The data mining (DM) has emerged as such tool that provide hidden information|s and patterns from customer data, and thus helps in organisation|s achieve an information|s which will provide customer satisfaction and increase customer base. The stiff competitions in insurance sector also demand that organisations should have cutting edge over their competitors. These requires the in depth analysis of the customer data using DM tools. We authors through this paper highlighted the importance and significance of DM techniques and tools in managing customer relationship management (CRM) by finding the hidden and unknown information from the real case data of insurance company. We had analysed through our research customer|s satisfaction level, churners and non-churners and much other information|s which will increase the profitability of the organisation and will provide customer satisfaction.

Olympics Big Data Prognostications

Data is continuously snowballing over the years, gradually a huge growth is seen in data to store and tame to yield meticulous result. It gives rise to a concept nowadays, reckoned as big data analytics. With the summer Olympics at Rio de Janeiro, Brazil in the year 2016 round the corner, we, the authors have implemented a mathematical model by implementing efficient map reduce program to predict the number of medals each country might bag at the games. Based on a number of factors such as historical performance of the country in terms of medals won, the performance of athletes, financial scenario in the country, fitness levels and nutrition of athletes along with familiarity to the playing conditions can be used to come up with a reliable estimate.

Fuzzy data mining: a literature survey and classification framework

Despite the tremendous application of data mining in business areas and simultaneous development in the area of fuzzy systems, both the technologies have never been able to prove worth in combination. The development in the application of fuzzy data mining is rapidly increasing and exhaustive literature survey in the field of fuzzy data mining has not been done in the past which prompted us to find and classify the existing literature in fuzzy data mining which will pave the way for further quality research in fuzzy data mining. The authors classified the available literature on three broad categories on the basis of data mining techniques which are applicable. The further classification is done on the basis on application areas where fuzzy data mining has found its worth.

Principles for successful mobile CRM in organisations from data mining perspective

The growing importance of customer-centric approach is evident from the fact that organisations started working towards the customers choices and preferences. The organisations need to interact more often with customers to understand their needs and preferences, which is possible only when the barriers of customer interactions are enhanced (e.g., accessing remotely located customer, etc.). The paper proposes the guiding principles for successful mobile CRM (mCRM) in organisation considering data mining (DM) tools and techniques. The gap between the combined use of proven technologies like wireless communication, DM tools and techniques in system like CRM is big which can be only narrowed through guiding principles. The approach to identify critical business principles for mCRM implementation is based on intensive information gathering process from officials and staff of customer-centric organisations like banking, retails, insurance and automobile which are very critical for successful implementation of mCRM in the organisation from DM perspective.