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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...
Understanding business behaviors requires acquiring huge amounts of data from diverse field studies. Location Based Social Networks can provide such large amounts of data that can be used in urban analysis to understand business behaviors. Towards more insight for business behavior, a novel analytical prespective that exploits data collected from Location Based Social Networks is introduced to predict...
This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization...
E-commerce is one of the fast growing business models of the present day. Shopping from anywhere & anytime has become a key feature of E-commerce. Because of the huge amount of data generated from the vast population of people & connected devices has posed challenges to manage the stream of data arriving from every device in real time. Big data analytics is a process of examining this big...
This paper attempts to understand the broader implications for integrating learning analytics in e-learning systems. This is also the commencement of a collaborative effort for developing research bases around learning analytics between the University of Mauritius and the University of KwaZulu-Natal. The focus on e-assessments is deliberate: the tendancy in both our universities to appraise student...
Big-data middle layer architecture is defined to perform the query analysis and the evaluation of the Big Data. This scheme is implemented on dynamic generated data section. The concept of Big Data concerns with a bulk of data presented in large volume with complicated architecture and with increasing data set. The data for such system can be taken from multiple sources and sometimes from independent...
With incoming data era from early 2010's, the word “Big Data” has been taken focus in many of research and development areas. The final purpose of Big Data research is the efficient utilization of useful information; however, the process involves a lot of complex subjects like data processing, data mining, and etc. In this paper, we summarize and discuss the subjects in area of Big Data analysis,...
Given the existence of a wide set of available data sources nowadays, the production of advanced and meaningful business analytics is considered of high importance by many SMEs and large companies, since it provides them the capacity to gain insight in future trends and proceed to better decision making. However, several challenges exist, relevant to the need for management of structured and unstructured...
The ongoing increase in the usage of web has led to accumulation of large amounts of data every second. This has in turn made the research industry to grow and focus towards employing web usage mining for increasing the revenues for businesses, carrying out analysis on browsing behavior of web users, improving website layout and much more. Web usage mining is becoming increasingly popular due to the...
Due to the increasing importance of trust in online transactions, the seller reputation system has become a widely used method for creating trust among the transactors and providing incentives for good behaviors in online transactions. A robust reputation system is considered crucial for building a better marketplace for various types of e-commerce. However, false reputation seriously affect the effectiveness...
The increasing availability of digital data offers new opportunities for analyzing business processes. Process aware information systems like Enterprise Resource Planning systems store data in the course of transaction processing. This data can be exploited by using process mining techniques. Process mining algorithms produce process models by analyzing recorded event logs. A fundamental challenge...
The traditional data warehouse (DW) can only analyze historical data and data extraction cycle is so long that it greatly reduces the enterprise's adaptability to change this situation. The 4-tier data warehouse architecture, which is based on multi-agent systems, not only improves the active and real-time performance of the data warehouse but also enhances the scalability of the system. In this way,...
The social networks have revolutionized the online communication and data sharing. The researchers are now focusing on mining and analysis of large amount of social network data for a variety of purposes. However, because of the huge amount of continuously changing data, the data analysis in a daunting task. OLAP analysis is a famous data analysis method which can be used to analyze social data. This...
Data analysis has been widely used in the enterprises for its high efficiency and accuracy, especially in the field of telecommunication industry, such as User Behavior Analysis, Customer Churn Prediction, etc. However, as the exponential growth of data, traditional data analysis tools can not handle such large-scale dataset. Furthermore, as business gets more and more complicated, there is much more...
Data mining an non-trivial extraction of novel, implicit, and actionable knowledge from large data sets is an evolving technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively. It is also known as Knowledge Discovery in Databases (KDD) and enables data exploration, data analysis, and data visualization of huge databases...
Metadata is the command center in the process of data warehousing,and which is very helpful to data ETL (Extraction, Transformation and Loading), data storage management,data analysis and data mining.CWM (Common Warehouse Meta-model) is an open industry metadata standard,and which is widely used currently,public meta-models and its rules defined in CWM can properly support data transformation and...
By tracing the evolution of the data mining based on the neural network algorithms in the e-commerce and identifying strategies being used nowadays, we will demonstrate how e-commerce enterprise can benefit by adopting strategies that harness the potential of data mining to improve the ability of prediction in their business activities.
This paper suggests the structure of patent data integration and analysis based on business intelligence (BI) to help enterprises make the effective decisions about patent strategy and orientation of technological development by extracting effective information from mass data. Firstly, the patent data is acquired from heterogeneous data sources into the local database. Then, we can load the business...
Information retrieval is the most popular database technology, which is focusing on data analysis, association rules, pattern discovery and so on. It is critical to find efficient ways of mining large data sets. In this paper we present a Personalized Travel Information System, called PTIS. PTIS can automatically link to the travel sites to collect information, and then create new data rules. According...
Hongqiao transportation hub combines variety of transportation ways and transfer plaza in one. Technical standard, interface manner and data format adopted by application systems of variety of transportation are non-unified. On the basic of the requirement of the information management, this paper gives a system framework of public information integration platform based on SOA, integrates multi-source...
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