The Importance and Problems of Big Data

In the era of high-tech we can hear the term Big Data more and more often. This fact indicates that the importance of Big Data constantly increases. This term is also used with related concepts such as Business Intelligence or data mining. But what does that mean

Platforms, Power, and the Antitrust Challenge: A Modest Proposal to Narrow the U.S.-Europe Divide

Big platforms dominate the new economy landscape. Colloquially known as GAFA [Google, Amazon, Facebook, and Apple] or FAANG [Facebook, Amazon, Apple, Netflix, and Google], the high tech big data companies are charged with using the power of their platforms to squelch start-ups, appropriate rivals’ ideas, and take and commercialize the personal data of their users. Are the platforms violating the antitrust laws? Should they be broken up? Or are they the agents of progress in the new economy? On these points, the United States antitrust law and the European Union competition law may diverge. The Competition Directorate-General of the European Commission has brought proceedings against or is investigating Google, Amazon, Apple, and Facebook. Germany, under its own competition law, has condemned Facebook’s conduct. Meanwhile, in the United States, authorities are skeptical, but they have commenced investigations. This Article is a comparative analysis of U.S. and EU law regarding monopolization/abuse of dominance as background to understanding why EU law is aggressive and U.S. law may be meek in the treatment of the big tech platforms. First, it examines the factors that underlie the two perspectives. Second, it considers three cases or problems—Google/Comparative Shopping (EU), Facebook-Personal Data (Germany), and dominant platforms’ acquisitions of start-ups that are inchoate competitive threats, such as Facebook’s acquisitions of WhatsApp and Instagram. The Article considers what lessons the latest Supreme Court antitrust decision, Ohio v. American Express (AmEx), holds for the analysis of the big data antitrust issues. Third, it asks what U.S. antitrust law and enforcement should do. It concludes that U.S. antitrust law should reclaim its role as watchdog to stop abuses of economic power, and makes suggestions for U.S. antitrust law to meet the big-platform challenge in a modest but meaningful and practicable way. I. Introduction II. A Brief Comparison of U.S. and EU Law of Monopolization/Abuse of Dominance ... A. The United States ... B. Europe ... C. Presumptions and Divergences III. Implications for High Tech, Big Data IV. Three Examples of Alleged Platform Abuse ... A. Google/Comparative Shopping ... 1. EU Law ... 2. U.S. Law ... B. Facebook—Abuse of Data ... 1. German Law ... 2. U.S. Law ... C. Start-Ups: Nipping Competition in the Bud V. Proposals VI. Conclusio

Multi-Objective Big Data Optimization with jMetal and Spark

Big Data Optimization is the term used to refer to optimization problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster computing system for solving multi-objective Big Data Optimization problems. Our purpose is to study the influence of accessing data stored in the Hadoop File System (HDFS) in each evaluation step of a metaheuristic and to provide a software tool to solve these kinds of problems. This tool combines the jMetal multi-objective optimization framework with Apache Spark. We have carried out experiments to measure the performance of the proposed parallel infrastructure in an environment based on virtual machines in a local cluster comprising up to 100 cores. We obtained interesting results for computational e ort and propose guidelines to face multi-objective Big Data Optimization problems.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Identifying Privacy Policy in Service Terms Using Natural Language Processing

Ever since technology (tech) companies realized that people\u27s usage data from their activities on mobile applications to the internet could be sold to advertisers for a profit, it began the Big Data era where tech companies collect as much data as possible from users. One of the benefits of this new era is the creation of new types of jobs such as data scientists, Big Data engineers, etc. However, this new era has also raised one of the hottest topics, which is data privacy. A myriad number of complaints have been raised on data privacy, such as how much access most mobile applications require to function correctly, from having access to a user\u27s contact list to media files. Furthermore, the level of tracking has reached new heights, from tracking mobile phone location, activities on search engines, to phone battery life percentage. However much data is collected, it is within the tech companies\u27 right to collect the data because they provide a privacy policy that informs the user on the type of data they collect, how they use that data, and how they share that data. In addition, we find that all privacy policies used in this research state that by using their mobile application, the user agrees to their terms and conditions. Most alarmingly, research done on privacy policies has found that only 9% of mobile app users read legal terms and conditions [2] because they are too long, which is a worryingly low number. Therefore, in this thesis, we present two summarization programs that take in privacy policy text as input and produce a shorter summarized version of the privacy policy. The results from the two summarization programs show that both implementations achieve an average of at least 50%, 90%, and 85% on the same sentence, clear sentence, and summary score grading metrics, respectively

Whalesong

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From opt-in to obligation? : Examining the regulation of globally operating tech companies through alternative regulatory instruments from a material and territorial viewpoint

Modern society’s ever-increasing reliance on technology raises complex legal challenges. In the search for an efficient and effective regulatory response, more and more authorities – in particular the European Union – are relying on alternative regulatory instruments (ARIs) when engaging big tech companies. Materially, this is a natural fit: the tech industry is a complex and rapidly-evolving sector and – unlike the rigid classic legislative process – ARIs allow for meaningful ex ante anticipatory constructions and ex post enforcement due to their unique flexibility. However, from a territorial point of view several complications arise. Although the use of codes of conduct to regulate transnational private actors has a rich history, the way in which such codes are set out under articles 40 and 41 of the EU’s GDPR implies a ‘hardening’ of these soft law instruments that has repercussions for their relationship to the principles of territorial jurisdiction. This contribution serves as a first step for further research into the relationship between codes of conduct, the regulation of the tech industry and the territorial aspects related thereto

Dynamic Multi-Objective Optimization With jMetal and Spark: a Case Study

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, we make use of the streaming facilities of Spark to feed an optimization problem with data from different sources. We demonstrate the use of our tool by solving a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on near real-time traffic data from New York City, which is updated several times per minute. Our experiment shows that both jMetal and Spark can be integrated providing a software platform to deal with dynamic multi-optimization problems.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Budidaya dan Keuntungan Petani Padi (Oryza Sativa L) di Desa Kiram Kecamatan Karang Intan Kabupaten Banjar Provinsi Kalimantan Selatan

To the effect of observational it to know technically paddy farming management and to know eknomis's ala hits cost, acceptance and paddy farming gain varietas Pandak at Silvan Kiram. This research utilize survey method with tech observation, panarikan samples to utilize simple random tech( Simple random is sampling) , Total varietas Pandak's paddy farmer at Silvan Kiram as much 102 person. Take at random simple( Simple Random is Sampling ) as much 20 % (20 person) of all population. Acquired production average as big as 3.497,40 kg / farmers. Accepting acquired acceptance paddy farmer average as big as Rp. 27. 979. 200,00 / Average gain which gotten by farmer as big as Rp. 15. 338. 796,62 / farmer

Keuntungan USAhatani Padi (Oryza Sativa L) di Desa Keladan Baru Kecamatan Gambut Kabupaten Banjar Provinsi Kalimantan Selatan

Research aims to know paddy farming management is sighted from technical aspect, know how big, acceptance and gain of Silvan paddy farming At Keladan Baru Village On this research meode which is utilized is with method surveys with observation tech, panarikan samples to utilize simple random tech( Simple random is sampling. Its outgrows total farmer that labour varietas\u27s paddy farming Unus\u27s Siam local as much 118 person, take at random simple( Simple Random is Sampling ) as much 25 % (30 person) of all population Acquired production of paddy farming average 2.679,00 kg / farmer or as big as 4.252,38 kg / ha (4,25 tons / ha) with average 267,90 blek / farmers or as big as 425,24 tin cans / ha. Acceptance on paddy farming average as big as Rp. 24. 111. 000,00 / farmer or Rp. 38. 271. 428,57 / ha. Averagely gain which gotten by farmer in one season plants out is as big as Rp. 19. 994. 267,93 / farmers or on a par as big as Rp. 31. 736. 933,23 / ha

KARAKTERISTIK PERUSAHAAN DAN PENGUNGKAPAN MODAL INTELEKTUAL: PERUSAHAAN-PERUSAHAAN DI INDONESIA TAHUN 2015

This empirical quantitative study seeks to examine the influence of company’s characteristics on intellectual capital disclosure. They consist of type of industry, leverage, listing age, and corporate governance attributes (ownership concentration and type of external auditor). Data were gathered from the annual reports of 82 publicly listed companies in Indonesian Stock Exchange in 2015. This study uses a stratified random sample of all companies listed in Indonesian Stock Exchange as the population. The 82 samples chosen based on their type of industry; 41 companies were chosen from high-tech industry and 41 companies were from low-tech industry. Data collection is limited to one year and only from annual reports. Multiple regression statistics analysis results reveal that high-tech and knowledge-intensive companies disclose more information about intellectual capital than low-tech companies do. Companies audited by big-four auditors also disclose more information than those without big-four auditors. Ownership concentration has negative significance association with the extent of intellectual capital disclosure. Level of leverage and listing age did not influence the occurrence of intellectual capital disclosure