Articles

A Generic Approach to Entity Resolution Mechanisms for Big Data on Real World Match Problems in the Global Oil and Gas Sector

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Complex challenges are facing the global oil and gas industry. Oil prices are dropping due to OPEC production level, US oil boom, and other factors. Many experts believe that prices of oil will remain low for years at equilibrium of around $40-50 (Blumberg, 2018; Walls and Zheng 2018; Azar, 2019). Although 2019 oil price is expected to average at $65 with a further decline at $62 by 2020 (Amadeo, 2019; Kasim, 2019). Also, newly commercial resources are extremely expensive to develop, as massive capital investments are required. This research intends to develop a comprehensive entity resolution framework that has the ability to search across multiple databases with disparate forms, tame large amounts of data very quickly, efficiently resolving multiple entities into one, as well as finding hidden connections without human intervention. Putting in place a system to manage these entities will not only help to better assign resources, but to do so in a more expedient fashion. Although the necessary information is mostly already available within the oil and gas companies, it is spread around different company areas and application. Entity resolution will helps to aggregate these data, identify and exploit connection between entities and offer holistic all-in-one information that can helps to identify and deal with potential risk. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from Opec database are not sufficiently solved with conventional approaches based on the similarity of attribute values.

Proposed Knowledge Management Design to Improve Business Processes at O Mart Retail Company

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Retail trade plays an important role both in a global context and in Indonesia itself because it drives economic growth, creates jobs and shapes consumer behavior. Understanding the importance of industry growth, dynamics of competition and knowledge management are essential for optimizing business performance and achieving sustainable success. Minimarket O Mart is one of the small retail in the form of minimarkets in Indonesia, minimarket O Mart has problems in its business, namely inefficient business because O Mart is still in its standard operating procedures, because there is still no good SOP documentation, besides that the knowledge sharing activities within the company is also limited to chatting and has not been carried out formally within the company. The aims of this study are to propose a knowledge management system that can improve business processes at O Mart and to develop an implementation plan for the suggested knowledge management system, outlining the steps and strategies required for its successful integration at O Mart. Theories that support this research are the Definition of Knowledge, Fishbone Analysis, Knowledge Management Framework, People-Process-Technology Framework, SECI Model, KM Roadmap, and Implementation Plan. The research methodology is based on a qualitative research design involving data collection through interviews with employees from various departments of O Mart Retail Company. This research uses a knowledge management framework (People, Process, Technology) and the SECI model which will then produce a Knowledge Management Roadmap and also a Knowledge management implementation plan which is expected to overcome the problem of inefficient business processes at O Mart minimarkets.

Growth Hack Service Framework for Boleh Dicoba Digital Company Based on Growth Hacking Framework and Value Co-Creation Framework

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BDD growth hack service practice is close to Bohnsack and Liesner (2019) and Kohtamäki & Rajala (2016) growth hack framework but lacking in data analysis and testing. The author suggested a new framework that combines the growth hack framework and the value co-creation framework. New operational instructions include value creation agreements and A/B testing based on comparative analysis. BDD should also remind clients about value creation and discuss value in exchange and value in use assignments. The new framework should increase service quality and resource availability of BDD growth hack service.