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Michal Koren,Elad Harison,Matan Shnaiderman 글로벌지식마케팅경영학회 2015 Global Fashion Management Conference Vol.2015 No.06
Fashion is primarily based on adoption of trends by consumers in textiles, clothing, footwear, jewelry and art, inter alia. As fashion is based on human preferences, it is characterized by dynamic changes throughout seasons and years, short life cycles, low predictability and high volatility of demand and impulse purchases. In the dynamic environment of apparel markets, fashion firms aim at successfully forecasting both the desirability of new collections and the volumes of each item produced and released to the market under terms of substantial levels of uncertainty. When demand for an item exceeds its supply, the firm is likely to lose additional profits that could have been collected had a sufficient volume of this item been present in the market. Alternatively, if the supply of an item surpassed its demand, it would remain unsold, thereby generating loss equal to its marginal production and distribution costs. The paper proposes a forecasting model that enhances the accuracy of fashion trend forecasting in the context of multiple variants of colour clothing. The model aims at maximizing profits of the firms, while minimizing the forecasting error and reducing the costs that result from excess capacity of production or, alternatively, from loss of potential revenues due to low demand.
Audrey V. Koren 한국로고스경영학회 2009 한국로고스경영학회 학술발표대회논문집 Vol.2009 No.7월
Thus in the modern tax laws there are no effective methods of the tax control which could be used with a view of revealing subjects of the electronic commerce evading from statement on the tax account or underestimating size of actually received incomes. Therefore creation of the effective mechanism of the taxation of subjects of the electronic commerce, considering unique features of electronic enterprise activity in a network the Internet. can increase considerably growth of tax revenues in budgetary system of Russia, China, India and South Korea.
Pig Vs. Hive Use Case Analysis
Danielle Kendal,Oded Koren,Nir Perel 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.12
Corporations are changing their practices to data-driven big data initiatives, as big data analytics has provided companies with the ability to grow their businesses and increase competition. As the importance of data analytics grew, so accordingly did the size of the data to analyze, thus demanding a more powerful data platform. This paper shows a case study of two High Level Query Languages that are constructed on top of Hadoop MapReduce; Pig and Hive. By creating a query in each query language, both resulting in an identical output, and by running each query 30 times on 2 different sized files (120 runs total), this comparison provides a statistically significant conclusion.
The Intended Consequences of More Frequent Portfolio Disclosure of Mutual Funds
Ji-Woong Chung(정지웅),Koren Jo,Jaeouk Kim(김재욱),Sejin Kang 한국경영학회 2021 한국경영학회 통합학술발표논문집 Vol.2021 No.8
We study the effect of increased portfolio transparency on mutual funds’ portfolio manipulation activities and investors’ capital allocation efficiency. Investigating the 2004 regulation change to increase the frequency of mutual fund portfolio disclosure in a difference-in-differences framework, we find that investment efficiency as measured by the return predictability of money flows has improved after the rule change. However, there is little evidence that portfolio manipulation practices - portfolio overlap, portfolio pumping, style drift, and window dressing - have declined after 2004. We conclude that a disclosure policy aiming to increase transparency about portfolio holdings enhances the information environment in the mutual fund market and enables investors to make more informed asset allocation decisions, but it is not sufficient to discourage fund managers from engaging in opportunistic behavior.
Zhuravlev, Yu.N.,Koren, O.G.,Reunova, G.D.,Artyukova, E.V.,Kozyrenko, M.M.,Muzarok, T.I.,Kats, I.L. The Korean Society of Ginseng 2004 Journal of Ginseng Research Vol.28 No.1
“The Regional complex long-term program of restoration (reintroduction) of Primoryes ginseng population up to 2005” elaborated by Primorye governor administration, Regional Committee of Natural Resources and Russian Academy of Sciences operates in Russian Primorye. The Institute of Biology and Soil Science (IBSS) provides the scientific implementation of the program including the genetic analysis of extant ginseng populations, plant reproduction and off-spring identification. According to our investigations, the genetic resource of P. ginseng in Primorye is represented by three populations of wild-growing ginseng and a few pritate plantations. The results obtained by RAPD allowed concluding that the resource is dispersed among the wild and cultivated ginseng sub-populations in such a way that each of sub-populations studied has to be represented as a stock material to maintain species genetic variability. The allozyme analyses also showed that the small sub-populations of wild ginseng are characterized by unique genetic diversity and, therefore, they all need to be represented in reintroduction centers. Additionally the allozyme analysis discovered that the Blue Mountain and Khasan populations possess the most genetic diversity. So, at least one more reproductive ginseng unit has to be created besides two already existing reintroduction centers representing the Sikhote-Alin and the Blue Mountain populations.
Zhuravlev Yu. N.,Koren O. G.,Reunova G. D.,Artyukova E. V.,Kozyrenko M. M.,Muzarok T. I.,Kats I. L. 고려인삼학회 2004 Journal of Ginseng Research Vol.28 No.1
"The Regional complex long-term program of restoration (reintroduction) of Primoryes ginseng population up to 2005" elaborated by Primorye governor administration, Regional Committee of Natural Resources and Russian Academy of Sciences operates in Russian Primorye. The Institute of Biology and Soil Science (IBSS) provides the scientific implementation of the program including the genetic analysis of extant ginseng populations, plant reproduction and offspring identification. According to our investigations, the genetic resource of P. ginseng in Primorye is represented by three populations of wild-growing ginseng and a few private plantations. The results obtained by RAPD allowed concluding that the resource is dispersed among the wild and cultivated ginseng sub-populations in such a way that each of sub-populations studied has to be represented as a stock material to maintain species genetic variability. The allozyme analyses also showed that the small sub-populations of wild ginseng are characterized by unique genetic diversity and, therefore. they all need to be represented in reintroduction centers. Additionally the allozyme analysis discovered that the Blue Mountain and Khasan populations possess the most genetic diversity. So, at least one more reproductive ginseng unit has to be created besides two already existing reintroduction centers representing the Sikhote-Alin and the Blue Mountain populations.
Big Data Performance Evaluation Analysis Using Apache Pig
Gal Engelberg,Oded Koren,Nir Perel 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.11
While companies' usage of big data products increases, the question of which big data architecture is the most suitable to the company's needs is rising. This study presents an approach of running multiple processes which simulates preliminary data processing of sale transactions input dataset using Apache Pig, in order to find the best performing big data environment in terms of decentralization level over the HDFS. The case study approach can provide companies an additional tool for understanding the required investment on hardware or cloud computing resources. We analyze which decentralization level achieves the best processing time, and explore the behavior of performance's change according to the change in decentralization level and performance change according to the change in the size of the input dataset. The case study's insights are: When processing the same data flow over the same input dataset, processing time performance is better as long as decentralization level increases; As long as decentralization level increases the change between performances decreases significantly; Processing the same Pig data flow under the same scale of decentralization level over large input dataset performs better then processing it over a smaller input dataset - in terms of processing time per volume unit; As blocks-data nodes ratio becomes higher, the processing time becomes longer, and vice versa.