Search results for: incomplete data extrapolation

Authors: Shizra Waris, Saleem Akhtar

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Big data is a research area receiving attention from academia and IT communities. In the digital world, the amounts of data produced and stored have within a short period of time. Consequently this fast increasing rate of data has created many challenges. In this paper, we use functionalism and structuralism paradigms to analyze the genesis of big data applications and its current trends. This paper presents a complete discussion on state-of-the-art big data technologies based on group and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also covers big data analytics techniques, processing methods, some reported case studies from different vendor, several open research challenges and the chances brought about by big data. The similarities and differences of these techniques and technologies based on important limitations are also investigated. Emerging technologies are suggested as a solution for big data problems.

Keywords: computer, it community, industry, big data

Procedia PDF Downloads 164

Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Puti F. Marzuki, Adrianto Oktavianus, Almerinda Regina

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Interface problems among interacting parties in Indonesian construction projects have most often led to low productivity and completion delay. In the midst of this country’s needs to accelerate construction of public infrastructure providing connectivity among regions and supporting economic growth as well as better living quality, project delays have to be seriously addressed. This paper identifies potential causes factors of interface problems experienced by construction projects in Indonesia. Data are collected through a survey involving the main actors of six important public infrastructure construction projects including railway, LRT, sports stadiums, apartment, and education building construction projects. Five of these projects adopt the design-build project delivery method and one applies the design-bid-build scheme. Interface problems’ potential causes are categorized into contract, management, technical experience, coordination, financial, and environmental factors. Research results reveal that, especially in railway and LRT projects, potential causes of interface problems are mainly technical and managerial in nature. These relate to complex construction execution in highly congested areas. Meanwhile, coordination cause factors are mainly found in the education building construction project with loan from a foreign donor. All of the six projects have to resolve interface problems caused by incomplete or low-quality contract documents. This research also shows that the design-bid-build delivery method involving more parties in construction projects tends to induce more interface problem cause factors than the design-build scheme.

Keywords: cause factors, construction delays, project delivery method, contract documents

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Authors: Branci Sarra, Branci Saadia

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Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining.

Keywords: high performance computing, HPC, big data, data analysis

Procedia PDF Downloads 489

Authors: Prashant Shrivastava

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The purpose of this study is to explore re3dat.org registry to identify research data repositories registration workflow process. Further objective is to depict a graph for present development of research data repositories in India. Preliminarily with an approach to understand re3data.org registry framework and schema design then further proceed to explore the status of research data repositories of India in re3data.org registry. Research data repositories are getting wider relevance due to e-research concepts. Now available registry re3data.org is a good tool for users and researchers to identify appropriate research data repositories as per their research requirements. In Indian environment, a compatible National Research Data Policy is the need of the time to boost the management of research data. Registry for Research Data Repositories is a crucial tool to discover specific information in specific domain. Also, Research Data Repositories in India have not been studied. Re3data.org registry and status of Indian research data repositories both discussed in this study.

Keywords: research data, research data repositories, research data registry, re3data.org

Procedia PDF Downloads 300

Authors: Ilgin Gökaşar, Saman Ghaffarian

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The need for fast processed big data of transportation ridership (eg., smartcard data) and traffic operation (e.g., traffic detectors data) which requires a lot of computational power is incontrovertible in Intelligent Transportation Systems. Nowadays cloud computing is one of the important subjects and popular information technology solution for data processing. It enables users to process enormous measure of data without having their own particular computing power. Thus, it can also be a good selection for transportation big data processing as well. This paper intends to examine how the cloud computing can enhance transportation big data process with contrasting its advantages and disadvantages, and discussing cloud computing features.

Keywords: big data, cloud computing, Intelligent Transportation Systems, ITS, traffic data processing

Procedia PDF Downloads 430

Authors: Ali Asheibi

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The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

Procedia PDF Downloads 223

Authors: E. Y. Igde, S. Aydogan, F. E. Boran, D. Akay

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Patent data have an increasingly important role in economic growth, innovation, technical advantages and business strategies and even in countries competitions. Analyzing of patent data is crucial since patents cover large part of all technological information of the world. In this paper, we have used the linguistic summarization technique to prove the validity of the hypotheses related to patent data stated in the literature.

Keywords: data mining, fuzzy sets, linguistic summarization, patent data

Procedia PDF Downloads 249

Authors: M. Kebisek, L. Spendla, M. Kopcek, T. Skulavik

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In our paper we describe the security capabilities of data collection. Data are collected with probes located in the near and distant surroundings of the company. Considering the numerous obstacles e.g. forests, hills, urban areas, the data collection is realized in several ways. The collection of data uses connection via wireless communication, LAN network, GSM network and in certain areas data are collected by using vehicles. In order to ensure the connection to the server most of the probes have ability to communicate in several ways. Collected data are archived and subsequently used in supervisory applications. To ensure the collection of the required data, it is necessary to propose algorithms that will allow the probes to select suitable communication channel.

Keywords: communication, computer network, data collection, probe

Procedia PDF Downloads 335

Authors: M. Pieńko, A. Robak, E. Błazik-Borowa, J. Szer

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This paper presents the results of analysis of 100 full-scale scaffolding structures in terms of compliance with legal acts and safety of use. In 2016 and 2017, authors examined scaffolds in Poland located at buildings which were at construction or renovation stage. The basic elements affecting the safety of scaffolding use such as anchors, supports, platforms, guardrails and toe-boards have been taken into account. All of these elements were checked in each of considered scaffolding. Based on the analyzed scaffoldings, the most common errors concerning assembly process and use of scaffolding were collected. Legal acts on the scaffoldings are not always clear, and this causes many issues. In practice, people realize how dangerous the use of incomplete scaffolds is only when the accident occurs. Despite the fact that the scaffolding should ensure the safety of its users, most accidents on construction sites are caused by fall from a height.

Keywords: façade scaffolds, load capacity, practice, safety of people

Procedia PDF Downloads 382

Authors: Fajana Sola Isaac

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In the last two decades, we have witnessed an increased interest in exchange rate pass-through (ERPT) in developing economies and emerging markets. This is perhaps due to the acknowledged significance of the pattern of exchange rate pass-through as a key instrument in monetary policy design, principally in retort to a shock in exchange rate in literature. This paper analyzed Exchange Rate Pass-Through by A Model of Oil Prices and Asymmetric Exchange Rate Fluctuations in Selected African Countries. The study adopted A Non-Linear Autoregressive Distributed Lag approach using yearly data on Algeria, Burundi, Nigeria and South Africa from 1986 to 2022. The paper found asymmetry in exchange rate pass-through in net oil-importing and net oil-exporting countries in the short run during the period under review. An ERPT exhibited a complete pass-through in the short run in the case of net oil-importing countries but an incomplete pass-through in the case of the net oil-exporting countries that were examined. An extended result revealed a significant impact of oil price shock on exchange rate pass-through to domestic price in the long run only for net oil importing countries. The Wald restriction test also confirms the evidence of asymmetric with the role of oil price acting as an accelerator to exchange rate pass-through to domestic price in the countries examined. The study found the outcome to be very useful for gaining expansive knowledge on the external shock impact on ERPT and could be of critical value for national monetary policy decisions on inflation targeting, especially for countries examined and other developing net oil importers and exporters.

Keywords: pass through, exchange rate, ARDL, monetary policy

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Authors: Luigi Pintacuda

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What characterize the phenomenology of Bucharest is that all operations of modernization have never been completed, creating a city made up of fragments. Understood this fragmented nature, the traces and fractures, the acceptance of their scars must represent the basis for the design of development for Bucharest. From this insight comes a new analysis of this city: a city of two million inhabitants that does not need a project on an urban scale (as all other major projects for the city have failed), but, starting from the study of all these interstitial spaces of public property, it must find its own strategy, a strategy on a large-scale that reflects on the sites on an architectural one. It is a city composed by fragments, not waste, but places for the project: ‘waiting spaces’ for a possible continuation of the process of genesis of a city which is often incomplete.

Keywords: public spaces, traces fractures, urban design, urban development

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Authors: Nay Myo Sandar

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With the growth of technologies and applications, a large amount of data has been producing at increasing rate from various resources such as social media networks, sensor devices, and other information serving devices. This large collection of massive, complex and exponential growth of dataset is called big data. The traditional database systems cannot store and process such data due to large and complexity. Consequently, cloud computing is a potential solution for data storage and processing since it can provide a pool of resources for servers and storage. However, moving large amount of data to and from is a challenging issue since it can encounter a high latency due to large data size. With respect to big data movement problem, this paper reviews the literature of previous works, discusses about research issues, finds out approaches for dealing with big data movement problem.

Keywords: Big Data, Cloud Computing, Big Data Movement, Network Techniques

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Authors: Julien Deborde, Thomas Milcent, Stéphane Glockner, Pierre Lubin

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A fully Eulerian method is developed to solve the problem of fluid-elastic structure interactions based on a 1-fluid method. The interface between the fluid and the elastic structure is captured by a level set function, advected by the fluid velocity and solved with a WENO 5 scheme. The elastic deformations are computed in an Eulerian framework thanks to the backward characteristics. We use the Neo Hookean or Mooney Rivlin hyperelastic models and the elastic forces are incorporated as a source term in the incompressible Navier-Stokes equations. The velocity/pressure coupling is solved with a pressure-correction method and the equations are discretized by finite volume schemes on a Cartesian grid. The main difficulty resides in that large deformations in the fluid cause numerical instabilities. In order to avoid these problems, we use a re-initialization process for the level set and linear extrapolation of the backward characteristics. First, we verify and validate our approach on several test cases, including the benchmark of FSI proposed by Turek. Next, we apply this method to study the wave damping phenomenon which is a mean to reduce the waves impact on the coastline. So far, to our knowledge, only simulations with rigid or one dimensional elastic structure has been studied in the literature. We propose to place elastic structures on the seabed and we present results where 50 % of waves energy is absorbed.

Keywords: damping wave, Eulerian formulation, finite volume, fluid structure interaction, hyperelastic material

Procedia PDF Downloads 297

Authors: Ahmed Mateen, Zhu Qingsheng, Ahmad Bilal

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In the current age of technology, information is the most precious asset of a company. Today, companies have a large amount of data. As the data become larger, access to data for some particular information is becoming slower day by day. Faster data processing to shape it in the form of information is the biggest issue. The major problems in distributed databases are the efficiency of data distribution and response time of data distribution. The security of data distribution is also a big issue. For these problems, we proposed a strategy that can maximize the efficiency of data distribution and also increase its response time. This technique gives better results for secure data distribution from multiple heterogeneous sources. The newly proposed technique facilitates the companies for secure data sharing efficiently and quickly.

Keywords: ER-schema, electronic record, P2P framework, API, query formulation

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Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

Procedia PDF Downloads 350

Authors: Michael Zaidman, Naum Simanovsky

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The most of brachial birth palsies are transient. Children with incomplete recovery almost always develop an internal rotation and adduction contracture. The muscle imbalance around the shoulder results in glenohumeral joint deformity and functional limitations. Natural history of glenohumeral deformity is it’s progression with worsening of function. Anteversion glenoid osteotomy with latissimus dorsi and teres major tendon transfers could be an alternative procedure of proximal humeral external rotation osteotomy for patients with severe glenohumeral dysplasia secondary to brachial plexus birth palsy. We will discuss pre-operative planning and stepped operative technique of the procedure on clinical example.

Keywords: obstetric brachial plexus palsy, glenoid anteversion osteotomy, tendon transfer, operative technique

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Authors: Mengdi Zhang, Zhenji Gao, Ning Kang, Rongmei Liu

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Management and application of geological big data is an important part of China's national big data strategy. With the implementation of a national big data strategy, geological big data management becomes more and more critical. At present, there are still a lot of technology barriers as well as cognition chaos in many aspects of geological big data management and application, such as data sharing, intellectual property protection, and application technology. Therefore, it’s a key task to make better use of new technologies for deeper delving and wider application of geological big data. In this paper, we briefly introduce the basic principle of blockchain technology at the beginning and then make an analysis of the application dilemma of geological data. Based on the current analysis, we bring forward some feasible patterns and scenarios for the blockchain application in geological big data and put forward serval suggestions for future work in geological big data management.

Keywords: blockchain, intellectual property protection, geological data, big data management

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

Procedia PDF Downloads 394

Authors: Hussaini Garba Mohammed

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Background/Significance: The lack of data gathering is affecting NGOs world-wide in general to have good data information about educational and health related issues among communities in any country and around the world. For example, HIV/AIDS smoking (Tuberculosis diseases) and COVID-19 virus carriers is becoming a serious public health problem, especially among old men and women. But there is no full details data survey assessment from communities, villages, and rural area in some countries to show the percentage of victims and patients, especial with this world COVID-19 virus among the people. These data are essential to inform programming targets, strategies, and priorities in getting good information about data gathering in any society.

Keywords: reliable information, data assessment, data mining, data communication

Procedia PDF Downloads 158

Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

Procedia PDF Downloads 825

Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

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Authors: Torsten Mielitz, Dietmar Schulz, York C. Roth

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Market forecasts show a significant increase in the demand for aircraft within the next two decades and production rates will be adapted accordingly. Improvements and optimizations in the industrial system are becoming more important to cope with future challenges in manufacturing and assembly. Highest quality standards have to be met for aerospace parts, whereas cost effective production in industrial systems and methodologies are also a key driver. A look at other industries like e.g., automotive shows well established processes to streamline existing manufacturing systems. In this paper, the implementation of 'PULL' principles in an existing manufacturing process chain for a large scale composite part is presented. A nonlinear extrapolation based on 'Little's Law' showed a risk of a significant increase of parts needed in the process chain to meet future demand. A project has been set up to mitigate the risk whereas the methodology has been changed from a traditional milestone approach in the beginning towards an agile way of working in the end in order to facilitate immediate benefits in the shop-floor. Finally, delivery rates could be increased avoiding more semi-finished parts in the process chain (work in progress & inventory) by the successful implementation of the 'PULL' philosophy in the shop-floor between the work stations. Lessons learned during the running project as well as implementation and operations phases are discussed in order to share best practices.

Keywords: aerospace composite part manufacturing, PULL principles, shop-floor implementation, lessons learned

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Authors: Wei-Hsuan Hsu, Yi-Xuan Huang

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Nanoimprint lithography (NIL) possesses the advantages of sub-10-nm feature and low cost. NIL patterns the resist with physical deformation using a mold, which can easily reproduce the required nano-scale pattern. However, the variation of process parameters and environmental conditions seriously affect reproduction quality. How to ensure the quality of imprinted pattern is essential for industry. In this study, the authors used the electrowetting technology to assist mold filling in the NIL process. A special mold structure was designed to cause electrowetting. During the imprinting process, when a voltage was applied between the mold and substrate, the hydrophilicity/hydrophobicity of the surface of the mold can be converted. Both simulation and experiment confirmed that the electrowetting technology can assist mold filling and avoid incomplete filling rate. The proposed method can also reduce the crack formation during the de-molding process. Therefore, electrowetting technology can improve the process quality of NIL.

Keywords: electrowetting, mold filling, nano-imprint, surface modification

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Authors: M. Naidoo

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Artificial intelligence often requires large amounts of good quality data. Within important fields, such as healthcare, the training of AI systems predominately relies on health and personal data; however, the usage of this data is complicated by various layers of law and ethics that seek to protect individuals’ privacy rights. This research seeks to establish the challenges AI and data sciences pose to (i) informational rights, (ii) privacy rights, and (iii) data protection. To solve some of the issues presented, various methods are suggested, such as embedding values in technological development, proper balancing of rights and interests, and others.

Keywords: artificial intelligence, data science, law, policy

Procedia PDF Downloads 87

Authors: Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

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In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data.

Keywords: simulation data, data summarization, spatial histograms, exploration, visualization

Procedia PDF Downloads 157

Authors: Hashem Dehghanniri

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Crime scripting is a simple and effective crime modeling technique that aims to improve understanding of security analysts about security and crime incidents. Low-quality scripts provide a wrong, incomplete, or sophisticated understanding of the crime commission process, which oppose the purpose of their application, e.g., identifying effective and cost-efficient situational crime prevention (SCP) measures. One important and overlooked factor in generating quality scripts is the crime scripting method. This study investigates the problems within the existing crime scripting practices and proposes a crime scripting approach that contributes to generating quality crime scripts. It was validated by experienced crime scripters. This framework helps analysts develop better crime scripts and contributes to their effective application, e.g., SCP measures identification or policy-making.

Keywords: attack modelling, crime commission process, crime script, situational crime prevention

Procedia PDF Downloads 104

Authors: Daijun Chen

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Affordable housing is subsidized by the government to meet the housing demand of low and middle-income urban residents in the process of urbanization and to alleviate the housing inequality caused by market-based housing reforms. It is a recognized fact that the living conditions of the insured have been improved while constructing the subsidized housing. However, the choice of affordable housing is mostly in the suburbs, where the surrounding urban functions and infrastructure are incomplete, resulting in the spatial mismatch of "jobs-housing" in affordable housing. The main reason for this problem is that the residents of affordable housing are more sensitive to the spatial location of their residence, but their selectivity and controllability to the housing location are relatively weak, which leads to higher commuting costs. Their real cost of living has not been effectively reduced. In this regard, 92 subsidized housing communities in Nanjing, China, are selected as the research sample in this paper. The residents of the affordable housing and their commuting Spatio-temporal behavior characteristics are identified based on the LBS (location-based service) data. Based on the spatial mismatch theory, spatial mismatch indicators such as commuting distance and commuting time are established to measure the spatial mismatch degree of subsidized housing in different districts of Nanjing. Furthermore, the geographically weighted regression model is used to analyze the influencing factors of the spatial mismatch of affordable housing in terms of the provision of employment opportunities, traffic accessibility and supporting service facilities by using spatial, functional and other multi-source Spatio-temporal big data. The results show that the spatial mismatch of affordable housing in Nanjing generally presents a "concentric circle" pattern of decreasing from the central urban area to the periphery. The factors affecting the spatial mismatch of affordable housing in different spatial zones are different. The main reasons are the number of enterprises within 1 km of the affordable housing district and the shortest distance to the subway station. And the low spatial mismatch is due to the diversity of services and facilities. Based on this, a spatial optimization strategy for different levels of spatial mismatch in subsidized housing is proposed. And feasible suggestions for the later site selection of subsidized housing are also provided. It hopes to avoid or mitigate the impact of "spatial mismatch," promote the "spatial adaptation" of "jobs-housing," and truly improve the overall welfare level of affordable housing residents.

Keywords: affordable housing, spatial mismatch, commuting characteristics, spatial adaptation, welfare benefits

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Authors: Vahid Bairami Rad

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Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented.

Keywords: spatial data base, knowledge discovery database, data mining, spatial relationship, predictive data mining

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Authors: Ghaleb Y. Abbasi, Sufyan Al-Mrayat

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This study examined the contribution of thirty-six factors to building construction project’s cost overrun in Jordan. A questionnaire was distributed to a random sample of 350 stakeholders comprised of owners, consultants, and contractors, of which 285 responded. SPSS analysis was conducted to identify the top five causes of cost overrun, which were a large number of variation orders, inadequate quantities provided in the contract, misunderstanding of the project plan, incomplete bid documents, and choosing the lowest price in the contract bidding. There was an agreement among the study participants in ranking the factors contributing to cost overrun, which indicated that these factors were very commonly encountered in most construction projects in Jordan. Thus, it is crucial to enhance the collaboration among the different project stakeholders to understand the project’s objectives and set a realistic plan that takes into consideration all the factors that might influence the project cost, which might eventually prevent cost overrun.

Keywords: cost, overrun, building construction projects, Jordan

Procedia PDF Downloads 74