scholarly journals Measuring Technology Platforms impact with search data and web scraping

2018 ◽  
Author(s):  
Desamparados Blazquez ◽  
Josep Domenech ◽  
José María García-Álvarez-Coque
Keyword(s):  
Web Scraping ◽  
Technology Platforms ◽  
Search Data

scholarly journals PENERAPAN TEKNIK WEB SCRAPING UNTUK PENGGALIAN POTENSI PAJAK (STUDI KASUS PADA ONLINE MARKET PLACE TOKOPEDIA, SHOPEE DAN BUKALAPAK)

2020 ◽  
Vol 13 (2) ◽  
pp. 65-75
Author(s):  
Mohammad Djufri
Keyword(s):  
Data Extraction ◽  
Quantitative Information ◽  
Policy Implications ◽  
The Internet ◽  
Market Place ◽  
Transaction Data ◽  
Intelligence System ◽  
Web Scraping ◽  
Directorate General ◽  
Search Data

Currently, millions of transaction data are avaliable on the internet, which can be retrieved and analyzed for excavating potential taxes. This article aims to examine whether the search data through web scraping techniques can be applied in an attempt to excavate the potential tax by the Account Representative. This paper uses an informetric approach, which will be examined quantitative information in the form of transaction data of sellers recorded on the three online marketplace (OMP) namely Tokopedia, Shopee and Bukalapak. The results show that web scraping techniques can be used for extracting potential taxes, and the best web scraping technique that can be done by the Directorate General of Taxation (DJP) is to develop its own integrated web scraping application as a Business Intelligence system. The results of this research are expected to contribute academically in the form of the use of web scraping in data extraction for the excavation of potential taxes and policy implications in terms of data search through the internet by the Directorate General of Taxation

Web scraping e diritti di proprietà intellettuale nell'intermediazione di biglietti aerei low cost

2018 ◽  
pp. 120-164
Author(s):  
Alessandra Corigliano
Keyword(s):  
Low Cost ◽  
Web Scraping

Nella sentenza di seguito commentata, la Corte d'Appello di Milano, in merito alla decisione di Ryanair di escludere qualsiasi intermediazione commerciale nella vendita dei propri biglietti aerei, si è pronunciata nella vertenza tra la compagnia aerea irlandese e l'agenzia di viaggi italiana Viaggiare che, in primo grado, ha denunciato il comportamento di Ryanair in quanto avrebbe ostacolato con il proprio comportamento l'agenzia di viaggio nella vendita dei biglietti aerei di Ryanair direttamente ai consumatori, costringendo l'agenzia stessa a riutilizzare i dati forniti dal database di Ryanair al fine di vendere indirettamente i biglietti sul suo sito web. La Corte (in parziale riforma della sentenza del Tribunale di primo grado) ha ritenuto che la decisione della compagnia aerea di riservarsi la vendita di biglietti aerei non costituisse un abuso di posizione dominante come previsto dall'articolo 102 del Trattato sul Funzionamento dell'Unione Europea, in quanto Ryanair deteneva nel mercato dei voli europei solo il 10%, quota questa molto bassa, che varrebbe a escludere una posizione dominante della compagnia su detto mercato. Nell'ottica della normativa antitrust, è stata accolta la mozione di Ryanair volta ad escludere una posizione dominante sul mercato dei voli europei, mentre nell'ottica dei diritti di proprietà intellettuale la domanda di Ryanair è stata respinta. A questo proposito, la Corte non ha accolto la mozione di Ryanair in base alla quale l'uso dei suoi marchi da parte di Viaggiare violasse i diritti privativi di Ryanair; la Corte ha inoltre stabilito che il database di Ryanair non potesse essere considerato di proprietà di quest'ultima, in quanto lo stesso, essendo del tutto svincolato da specifiche tecniche e funzionali che ne dettano la scelta e l'organizzazione dei dati, non può essere considerato alla stregua di una manifestazione creativa e, quindi, proprietà intellettuale ai sensi dell'art. 2, 64-quinques e 64-sexies della Legge sul Copyright. La Corte ha quindi ritenuto che non vi fosse nemmeno protezione ai sensi della cosiddetta dottrina "sui generis" del database Rynair poiché la protezione di tale database era finalizzata ad escludere la commercializzazione dei biglietti aerei e non a proteggere gli sforzi di investimento di Ryanair. La condotta di Viagiare di "screen scraping" dei dati Ryanair relativi all'offerta di biglietti aerei è stata considerata legittima in quanto Ryanair - nei Termini di Utilizzo del suo sito web - ha fornito l'accesso (concessione di licenza) a terzi dei suoi dati

Using Big Search Data to Map Your Market: Marketing in a Digital Age

IESE Insight ◽  
2017 ◽  
pp. 31-37 ◽  
Author(s):  
Berndt Skiera ◽  
Daniel M. Ringel
Keyword(s):  
Digital Age ◽  
Search Data

The impact of Airbnb on a non-touristic city. A Case study of short-term rentals in Santa Cruz de Tenerife (Spain)

Erdkunde ◽  
2020 ◽  
Vol 74 (3) ◽  
pp. 191-204
Author(s):  
Marcus Hübscher ◽  
Juana Schulze ◽  
Felix zur Lage ◽  
Johannes Ringel
Keyword(s):  
Santa Cruz ◽  
Short Term ◽  
Rental Market ◽  
Rural Space ◽  
Use Of Data ◽  
Web Scraping ◽  
Per Capita ◽  
Per Se ◽  
The Impact

Short-term rentals such as Airbnb have become a persistent element of today’s urbanism around the globe. The impacts are manifold and differ depending on the context. In cities with a traditionally smaller accommodation market, the impacts might be particularly strong, as Airbnb contributes to ongoing touristification processes. Despite that, small and medium-sized cities have not been in the centre of research so far. This paper focuses on Santa Cruz de Tenerife as a medium-sized Spanish city. Although embedded in the touristic region of the Canary Islands, Santa Cruz is not a tourist city per se but still relies on touristification strategies. This paper aims to expand the knowledge of Airbnb’s spatial patterns in this type of city. The use of data collected from web scraping and geographic information systems (GIS) demonstrates that Airbnb has opened up new tourism markets outside of the centrally established tourist accommodations. It also shows that the price gap between Airbnb and the housing rental market is broadest in neighbourhoods that had not experienced tourism before Airbnb entered the market. In the centre the highest prices and the smallest units are identified, but two peripheral quarters stand out. Anaga Mountains, a natural and rural space, has the highest numbers of Airbnb listings per capita. Suroeste, a suburban quarter, shows the highest growth rates on the rental market, which implies a linkage between Airbnb and suburbanization processes.

Research for a sustainable European sugar sector

2010 ◽  
pp. 487-495
Author(s):  
Martin Bruhns ◽  
Peter Glaviè ◽  
Arne Sloth Jensen ◽  
Michael Narodoslawsky ◽  
Giorgio Pezzi ◽  
...  
Keyword(s):  
Sugar Industry ◽  
Research Priorities ◽  
Decision Makers ◽  
Sustainable Chemistry ◽  
Technology Platforms ◽  
Research Themes ◽  
Starting Point ◽  
Manufacturing Applications ◽  
European Technology

The paper is based on the results of international project entitled “Towards Sustainable Sugar Industry in Europe (TOSSIE)”. 33 research topics of major importance to the sugar sector are listed and briefly described, and compared with research priorities of the European Technology Platforms: “Food for Life”, “Sustainable Chemistry”, “Biofuels”, and “Plant for the Future”. Most topics are compatible with the research themes included in the COOPERATION part of the 7th Framework Program of the EU (2007-2013). However, some topics may require long-term R&D with the time horizon of up to 15 years. The list of topics is divided into four parts: Sugar manufacturing, Applications of biotechnology and biorefinery processing, Sugarbeet breeding and growing, Horizontal issues. Apart from possible use of the list by policy- and decision makers with an interest in sugarbeet sector, the description of each research topic can be used as a starting point in setting up a research project or other R&D activities.

Pemanfaatan Sistem Informasi Terhadap Kinerja Pelayanan, Sirkulasi Buku Dan Pengolahan Arsip

2018 ◽  
Vol 4 (1) ◽  
pp. 87-96
Author(s):  
Yanni Suherman
Keyword(s):  
Information System ◽  
Data Processing ◽  
System Development ◽  
Processing System ◽  
Data Processing System ◽  
Data Archiving ◽  
Data Archives ◽  
Development Life Cycle ◽  
Search Data

Research conducted at the Office of Archives and Library of Padang Pariaman Regency aims to find out the data processing system library and data archiving. All data processing is done is still very manual by using the document in writing and there is also a stacking of archives on the service. By utilizing library information systems and archives that will be applied to the Office of Archives and Library of Padang Pariaman Regency can improve the quality of service that has not been optimal. This research was made by using System Development Life Cycle (SDLC) which is better known as waterfall method. The first step taken on this method is to go directly to the field by conducting interviews and discussions. This information system will be able to assist the work of officers in terms of data processing libraries and facilitate in search data archives by presenting reports more accurate, effective and efficient.

Unbiased Learning to Rank

2021 ◽  
Vol 39 (2) ◽  
pp. 1-29
Author(s):  
Qingyao Ai ◽  
Tao Yang ◽  
Huazheng Wang ◽  
Jiaxin Mao
Keyword(s):  
Online Learning ◽  
Theoretical Foundation ◽  
Learning To Rank ◽  
Research Question ◽  
Parameters Estimation ◽  
User Interactions ◽  
Empirical Performance ◽  
Search Data ◽  
Two Sides ◽  
Important Research Question

How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning algorithms with logged data, namely, the offline unbiased learning, and the studies on unbiased parameters estimation with real-time user interactions, namely, the online learning to rank. While their definitions of unbiasness are different, these two types of ULTR algorithms share the same goal—to find the best models that rank documents based on their intrinsic relevance or utility. However, most studies on offline and online unbiased learning to rank are carried in parallel without detailed comparisons on their background theories and empirical performance. In this article, we formalize the task of unbiased learning to rank and show that existing algorithms for offline unbiased learning and online learning to rank are just the two sides of the same coin. We evaluate eight state-of-the-art ULTR algorithms and find that many of them can be used in both offline settings and online environments with or without minor modifications. Further, we analyze how different offline and online learning paradigms would affect the theoretical foundation and empirical effectiveness of each algorithm on both synthetic and real search data. Our findings provide important insights and guidelines for choosing and deploying ULTR algorithms in practice.

Color Trend Analysis using Machine Learning with Fashion Collection Images

2021 ◽  
pp. 0887302X2199594
Author(s):  
Ahyoung Han ◽  
Jihoon Kim ◽  
Jaehong Ahn
Keyword(s):  
Machine Learning ◽  
Trend Analysis ◽  
Image Data ◽  
Fashion Industry ◽  
Color Palette ◽  
Web Scraping ◽  
Design Variables ◽  
Sales Organizations ◽  
Fashion Designers ◽  
Selection Of

Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

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