331,633 research outputs found

    Discovering sequential rental patterns by fleet tracking

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    © Springer International Publishing Switzerland 2015. As one of the most well-known methods on customer analysis, sequential pattern mining generally focuses on customer business transactions to discover their behaviors. However in the real-world rental industry, behaviors are usually linked to other factors in terms of actual equipment circumstance. Fleet tracking factors, such as location and usage, have been widely considered as important features to improve work performance and predict customer preferences. In this paper, we propose an innovative sequential pattern mining method to discover rental patterns by combining business transactions with the fleet tracking factors. A novel sequential pattern mining framework is designed to detect the effective items by utilizing both business transactions and fleet tracking information. Experimental results on real datasets testify the effectiveness of our approach

    THE IDENTIFICATION OF MONEY LAUNDERING ON DRUG TRAFFICKING

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    Drug trafficking is a holistic process. The involvement of various parties linked with law enforcement efforts. The number of drug trade reaches millilons of tons. This number equivalent to billions of fund that circulating in trading transactions. The one alternative ways to eradicate drug trafficking is to track the cash flow. Because of the financial transactions as the main chain to running this business. Tracking the flow of drug trafficking funds through money laundering approach can be done by indetifying various placement, layering, and integration activities of suspicious financial transactions. The placement pattern becomes the entrance of financial transactions through the participation of the providers of financial services. The layering pattern can consist od smurfing, money changer, and buying a stock portofolio. Meanwhile, the integration pattern enters various business activities with minimal risk. The prediction of money laundering tren based on drug trafficking leads three aspects, that is utilization of technologies, the role of third parties, and the involvement of unscrupulous government and law enforcement

    Business Sustainability in the Era of Society 5.0: Optimizing the Utilization of Social Media and Fintech for Muslim Millennial Entrepreneurs

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    The transformation of conventional business transactions into digital-based businesses is strongly influenced by the presence of social media and fintech. The pattern of business interactions does not only take place in traditional and modern markets, but has also penetrated the virtual world. This study aims to examine and explore efforts to optimize the use of social media and fintech for business actors from Muslim millennials in maintaining the sustainability of the business they are engaged in in the face of the Society 5.0 era. This type of research is a field research with a qualitative approach with a descriptive analysis method with a case study model on Muslim millennial business actors carried out in the Bungku Utara district, Morowali, Central Sulawesi. The result found that the use of social media in the form of Facebook, WhatsApp and Instagram as well as fintech in the form of M-Banking and E-Wallet for business continuity, plays an important role and its use in marketing, sales, market analysis, and forming and developing business networks. The theoretical implications of this research show that there is a need to integrate the use of social media and fintech in business activities by business actors. The practical implications show that the greater the use of social media in business activities, the greater the use of fintech which will support business continuity managed by business actors

    Parallel Implementation of Apriori Algorithm on Multicore System for Retail Market

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    Data Mining is a process of examining data and revealing the interesting patterns which are hidden. Association Rule Mining is a key technique of data mining. This technique works on finding intriguing relationships. Association rules are generated using Apriori Algorithm. The set of data includes a number of items which are called transactions. The work of this algorithm is to produce frequent itemsets from the transactional databases based upon the minimum support value. The outcome of an Apriori Algorithm is sets of association rules that provide us the frequency of items that are contained in sets of data which provide us the hidden pattern and general trends. This will be helpful for the retailer be familiar with the market and customer’s purchasing behavior. In pursuance of finding more valuable rules, our basic aim is to implement Apriori Algorithm using multithreading approach which can utilization our multicore processing system to improve the performance of an algorithm in a practical and efficient way to unearth more value information for the proper analysis of the business trends

    The transaction pattern through automating TrAM

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    Transaction Agent Modelling (TrAM) has demonstrated how the early requirements of complex enterprise systems can be captured and described in a lucid yet rigorous way. Using Geerts and McCarthy’s REA (Resource-Events-Agents) model as its basis, the TrAM process manages to capture the ‘qualitative’ dimensions of business transactions and business processes. A key part of the process is automated model-checking, which CG has revealed to be beneficial in this regard. It enables models to retain the high-level business concepts yet providing a formal structure at that high-level that is lacking in Use Cases. Using a conceptual catalogue informed by transactions, we illustrate the automation of a transaction pattern from which further specialisations impart a tested specification for system implementation, which we envisage as a multi-agent system in order to reflect the dynamic world of business activity. It would furthermore be able to interoperate across business domains as they would share the generalised TM as a pattern.</p

    GOOGLE ADSENSE PERSPEKTIF HUKUM PERJANJIAN ISLAM

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    This research examines is about the mechanism of the Google Adsense contract viewed from the legal aspects of the Islamic agreement. Google Adsense mechanism is different from business mechanisms in general, the differentiation is seen from the involvement of advertisers, Google and publishers who are bound in an online contract system. The purpose of this research is to find out how the mechanism of Google Adsense, besides analyzing the legal theory of Islamic agreements on the mechanism of Google Adsense. Type of research is a descriptive-analytical field research using a pattern of Islamic legal approach. There are two theories on which this research is based, namely contract theory and online business theory for analyzing legal aspects. The contract theory used focuses on the theory of the muamalat legal perspective contract which is then used to analyze transactions in Google Adsense, then from that analysis will be known how the law of the Google Adsense business. The results of this study indicate that the mechanism that occurs in Google Adsense reflects the basic values of the contract that are in accordance with the rules of the contract law. Standard contract enforcement aims to avoid moral hazard to protect the parties involved in online business for the realization of mutual benefit and prosperity. In addition, the screening efforts imposed on Google Adsense aim to demonstrate the application of business ethics values, which are known to have implications for the validity of the contract (in accordance with Islamic treaty law) 

    Using webcrawling of publicly available websites to assess E-commerce relationships

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    We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation

    SODA: Generating SQL for Business Users

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    The purpose of data warehouses is to enable business analysts to make better decisions. Over the years the technology has matured and data warehouses have become extremely successful. As a consequence, more and more data has been added to the data warehouses and their schemas have become increasingly complex. These systems still work great in order to generate pre-canned reports. However, with their current complexity, they tend to be a poor match for non tech-savvy business analysts who need answers to ad-hoc queries that were not anticipated. This paper describes the design, implementation, and experience of the SODA system (Search over DAta Warehouse). SODA bridges the gap between the business needs of analysts and the technical complexity of current data warehouses. SODA enables a Google-like search experience for data warehouses by taking keyword queries of business users and automatically generating executable SQL. The key idea is to use a graph pattern matching algorithm that uses the metadata model of the data warehouse. Our results with real data from a global player in the financial services industry show that SODA produces queries with high precision and recall, and makes it much easier for business users to interactively explore highly-complex data warehouses.Comment: VLDB201

    Goal-Oriented RE for E-Services

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    Current research in service-oriented computing (SoC) is mainly\ud about technology standards for SoC and the design of software components that\ud implement these standards. In this paper we investigate the problem of\ud requirements engineering (RE) for SoC. We propose a framework for goaloriented\ud RE for e-services that identifies patterns in service provisioning and\ud shows how to compose business models from them. Based on an analysis of 19\ud business models for e-intermediaries we identified 10 intermediation service\ud patterns and their goals, and show how we can compose new business models\ud from those patterns in a goal-oriented way. We represent the service patterns\ud using value models, which are models that show which value exchanges\ud business patterns engage in. We conclude the paper with a discussion of how\ud this approach can be extended to include business process patterns to perform\ud the services, and software components that support these processes
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