DATA SCIENCE CONFERENCE


Data Science Conference is one of the leading research topics in the international research conference domain. Data Science is a conference track under the Architecture and Urban Planning Conference which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Architecture and Urban Planning.

internationalconference.net provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of (Architecture and Urban Planning).

Data Science is not just a call for academic papers on the topic; it can also include a conference, event, symposium, scientific meeting, academic, or workshop.

You are welcome to SUBMIT your research paper or manuscript to Data Science Conference Track will be held at “Architecture and Urban Planning Conference in Paris, France in June 2019” - “Architecture and Urban Planning Conference in London, United Kingdom in August 2019” - “Architecture and Urban Planning Conference in New York, United States in October 2019” - “Architecture and Urban Planning Conference in Rome, Italy in December 2019” - “Architecture and Urban Planning Conference in London, United Kingdom in February 2020” - “Architecture and Urban Planning Conference in Barcelona, Spain in April 2020” .

Data Science is also a leading research topic on Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, Elsevier, Scopus, Web of Science.

INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

JUNE 26 - 27, 2019
PARIS, FRANCE

INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

AUGUST 21 - 22, 2019
LONDON, UNITED KINGDOM

  • Abstracts/Full-Text Paper Submission Deadline May 30, 2019
  • Notification of Acceptance/Rejection Deadline June 13, 2019
  • Final Paper and Early Bird Registration Deadline July 22, 2019
  • CONFERENCE CODE: 19AUPC08GB
  • One Time Submission Deadline Reminder

INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

OCTOBER 09 - 10, 2019
NEW YORK, UNITED STATES

  • Abstracts/Full-Text Paper Submission Deadline May 30, 2019
  • Notification of Acceptance/Rejection Deadline June 13, 2019
  • Final Paper and Early Bird Registration Deadline September 09, 2019
  • CONFERENCE CODE: 19AUPC10US
  • One Time Submission Deadline Reminder

INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

DECEMBER 11 - 12, 2019
ROME, ITALY

  • Abstracts/Full-Text Paper Submission Deadline May 30, 2019
  • Notification of Acceptance/Rejection Deadline June 13, 2019
  • Final Paper and Early Bird Registration Deadline November 12, 2019
  • CONFERENCE CODE: 19AUPC12IT
  • One Time Submission Deadline Reminder

INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

FEBRUARY 18 - 19, 2020
LONDON, UNITED KINGDOM

  • Abstracts/Full-Text Paper Submission Deadline May 30, 2019
  • Notification of Acceptance/Rejection Deadline June 13, 2019
  • Final Paper and Early Bird Registration Deadline January 16, 2020
  • CONFERENCE CODE: 20AUPC02GB
  • One Time Submission Deadline Reminder

INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

APRIL 15 - 16, 2020
BARCELONA, SPAIN

  • Abstracts/Full-Text Paper Submission Deadline May 30, 2019
  • Notification of Acceptance/Rejection Deadline June 13, 2019
  • Final Paper and Early Bird Registration Deadline March 16, 2020
  • CONFERENCE CODE: 20AUPC04ES
  • One Time Submission Deadline Reminder
FINISHED

INTERNATIONAL ARCHITECTURE AND URBAN PLANNING CONFERENCE

MARCH 19 - 20, 2019
ISTANBUL, TURKEY

Architecture and Urban Planning Conference Call For Papers are listed below:

Previously Published Papers on "Data Science Conference"

  • Automatic Thresholding for Data Gap Detection for a Set of Sensors in Instrumented Buildings
    Authors: Houda Najeh, Stéphane Ploix, Mahendra Pratap Singh, Karim Chabir, Mohamed Naceur Abdelkrim, Keywords: Building system, time series, diagnosis, outliers, delay, data gap. DOI:10.5281/zenodo.2571656 Abstract: Building systems are highly vulnerable to different kinds of faults and failures. In fact, various faults, failures and human behaviors could affect the building performance. This paper tackles the detection of unreliable sensors in buildings. Different literature surveys on diagnosis techniques for sensor grids in buildings have been published but all of them treat only bias and outliers. Occurences of data gaps have also not been given an adequate span of attention in the academia. The proposed methodology comprises the automatic thresholding for data gap detection for a set of heterogeneous sensors in instrumented buildings. Sensor measurements are considered to be regular time series. However, in reality, sensor values are not uniformly sampled. So, the issue to solve is from which delay each sensor become faulty? The use of time series is required for detection of abnormalities on the delays. The efficiency of the method is evaluated on measurements obtained from a real power plant: an office at Grenoble Institute of technology equipped by 30 sensors.
  • Estimation of Train Operation Using an Exponential Smoothing Method
    Authors: Taiyo Matsumura, Kuninori Takahashi, Takashi Ono, Keywords: Exponential smoothing method, open data, operation estimation, train schedule. DOI:10.5281/zenodo.1316818 Abstract: The purpose of this research is to improve the convenience of waiting for trains at level crossings and stations and to prevent accidents resulting from forcible entry into level crossings, by providing level crossing users and passengers with information that tells them when the next train will pass through or arrive. For this paper, we proposed methods for estimating operation by means of an average value method, variable response smoothing method, and exponential smoothing method, on the basis of open data, which has low accuracy, but for which performance schedules are distributed in real time. We then examined the accuracy of the estimations. The results showed that the application of an exponential smoothing method is valid.
  • A Study of the Adaptive Reuse for School Land Use Strategy: An Application of the Analytic Network Process and Big Data
    Authors: Wann-Ming Wey, Keywords: Adaptive reuse, analytic network process, big data, land use strategy. DOI:10.5281/zenodo.1315635 Abstract: In today's popularity and progress of information technology, the big data set and its analysis are no longer a major conundrum. Now, we could not only use the relevant big data to analysis and emulate the possible status of urban development in the near future, but also provide more comprehensive and reasonable policy implementation basis for government units or decision-makers via the analysis and emulation results as mentioned above. In this research, we set Taipei City as the research scope, and use the relevant big data variables (e.g., population, facility utilization and related social policy ratings) and Analytic Network Process (ANP) approach to implement in-depth research and discussion for the possible reduction of land use in primary and secondary schools of Taipei City. In addition to enhance the prosperous urban activities for the urban public facility utilization, the final results of this research could help improve the efficiency of urban land use in the future. Furthermore, the assessment model and research framework established in this research also provide a good reference for schools or other public facilities land use and adaptive reuse strategies in the future.
  • Integration of Big Data to Predict Transportation for Smart Cities
    Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin, Keywords: Big data, bus headway prediction, machine learning, public transportation. DOI:10.5281/zenodo.1315633 Abstract: The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.
  • Elemental Graph Data Model: A Semantic and Topological Representation of Building Elements
    Authors: Yasmeen A. S. Essawy, Khaled Nassar, Keywords: Building information modeling, elemental graph data model, geometric and topological data models, and graph theory. DOI:10.5281/zenodo.1132216 Abstract: With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.
  • Exploring the Activity Fabric of an Intelligent Environment with Hierarchical Hidden Markov Theory
    Authors: Chiung-Hui Chen, Keywords: Behavior, big data, hierarchical Hidden Markov Model, intelligent object. DOI:10.5281/zenodo.1132152 Abstract: The Internet of Things (IoT) was designed for widespread convenience. With the smart tag and the sensing network, a large quantity of dynamic information is immediately presented in the IoT. Through the internal communication and interaction, meaningful objects provide real-time services for users. Therefore, the service with appropriate decision-making has become an essential issue. Based on the science of human behavior, this study employed the environment model to record the time sequences and locations of different behaviors and adopted the probability module of the hierarchical Hidden Markov Model for the inference. The statistical analysis was conducted to achieve the following objectives: First, define user behaviors and predict the user behavior routes with the environment model to analyze user purposes. Second, construct the hierarchical Hidden Markov Model according to the logic framework, and establish the sequential intensity among behaviors to get acquainted with the use and activity fabric of the intelligent environment. Third, establish the intensity of the relation between the probability of objects’ being used and the objects. The indicator can describe the possible limitations of the mechanism. As the process is recorded in the information of the system created in this study, these data can be reused to adjust the procedure of intelligent design services.
  • Assessment of the Number of Damaged Buildings from a Flood Event Using Remote Sensing Technique
    Authors: Jaturong Som-ard, Keywords: Flooding extent, Sentinel-1A data, JOSM desktop, damaged buildings. DOI:10.5281/zenodo.1132092 Abstract: The heavy rainfall from 3rd to 22th January 2017 had swamped much area of Ranot district in southern Thailand. Due to heavy rainfall, the district was flooded which had a lot of effects on economy and social loss. The major objective of this study is to detect flooding extent using Sentinel-1A data and identify a number of damaged buildings over there. The data were collected in two stages as pre-flooding and during flood event. Calibration, speckle filtering, geometric correction, and histogram thresholding were performed with the data, based on intensity spectral values to classify thematic maps. The maps were used to identify flooding extent using change detection, along with the buildings digitized and collected on JOSM desktop. The numbers of damaged buildings were counted within the flooding extent with respect to building data. The total flooded areas were observed as 181.45 sq.km. These areas were mostly occurred at Ban khao, Ranot, Takhria, and Phang Yang sub-districts, respectively. The Ban khao sub-district had more occurrence than the others because this area is located at lower altitude and close to Thale Noi and Thale Luang lakes than others. The numbers of damaged buildings were high in Khlong Daen (726 features), Tha Bon (645 features), and Ranot sub-district (604 features), respectively. The final flood extent map might be very useful for the plan, prevention and management of flood occurrence area. The map of building damage can be used for the quick response, recovery and mitigation to the affected areas for different concern organization.
  • Forthcoming Big Data on Smart Buildings and Cities: An Experimental Study on Correlations among Urban Data
    Authors: Yu-Mi Song, Sung-Ah Kim, Dongyoun Shin, Keywords: Big data, correlation analysis, data recommendation system, urban data network. DOI:10.5281/zenodo.1130489 Abstract: Cities are complex systems of diverse and inter-tangled activities. These activities and their complex interrelationships create diverse urban phenomena. And such urban phenomena have considerable influences on the lives of citizens. This research aimed to develop a method to reveal the causes and effects among diverse urban elements in order to enable better understanding of urban activities and, therefrom, to make better urban planning strategies. Specifically, this study was conducted to solve a data-recommendation problem found on a Korean public data homepage. First, a correlation analysis was conducted to find the correlations among random urban data. Then, based on the results of that correlation analysis, the weighted data network of each urban data was provided to people. It is expected that the weights of urban data thereby obtained will provide us with insights into cities and show us how diverse urban activities influence each other and induce feedback.
  • Urban Big Data: An Experimental Approach to Building-Value Estimation Using Web-Based Data
    Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin, Keywords: Big data, building-value analysis, machine learning, price prediction. DOI:10.5281/zenodo.1130307 Abstract: Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.
  • Exploring Influence Range of Tainan City Using Electronic Toll Collection Big Data
    Authors: Chen Chou, Feng-Tyan Lin, Keywords: Big Data, ITS, influence range, living area, central place theory, visualization. DOI:10.5281/zenodo.1129704 Abstract: Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.