Databases and Information Systems Commons^™

A Model Of Competition Between Perpetual Software And Software As A Service, Zhiling Guo, Dan Ma

Research Collection School Of Information Systems

Software as a service (SaaS) has grown to be a significant segment of many software product markets. SaaS vendors, which charge customers based on use and continuously improve the quality of their products, have put competitive pressure on traditional perpetual software vendors, which charge a licensing fee and periodically upgrade the quality of their software. We develop an analytical model to study the competitive pricing strategies of an incumbent perpetual software vendor in the presence of a SaaS competitor. We find that, depending on both the SaaS quality improvement rate and the network effect, the perpetual software vendor adopts one ...

A Model Of Competition Between Perpetual Software And Software As A Service, Zhiling Guo, Dan Ma

Research Collection School Of Information Systems

Software as a service (SaaS) has grown to be a significant segment of many software product markets. SaaS vendors, which charge customers based on use and continuously improve the quality of their products, have put competitive pressure on traditional perpetual software vendors, which charge a licensing fee and periodically upgrade the quality of their software. We develop an analytical model to study the competitive pricing strategies of an incumbent perpetual software vendor in the presence of a SaaS competitor. We find that, depending on both the SaaS quality improvement rate and the network effect, the perpetual software vendor adopts one ...

Modeling Habitat Suitability Of Invasive Carps In The Upper Missisisippi River System, Charlotte Alexander, Robert F. Allen, Kevin J. Aagard

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.

Systematic Analysis Of Enterprise Perception Towards Cloud Adoption In The African States: The Nigerian Perspective, George A. Oguntala, Prof. Raed A. Abd-Alhameed, Dr. Janet O. Odeyemi

The African Journal of Information Systems

The desirous benefits of cloud computing such as high return on investment through efficient resource management, high application throughput and on-demand capabilities have resulted in the unprecedented global acceptance of the computing paradigm. However, research on cloud adoption indicates that fewer organisations in the African states are adopting cloud services. Thus, the purview of the paper is to examine the factors responsible for the poor adoption of cloud computing in most African enterprises using Nigeria as a case study. The study focus on the perception of IT and non-IT employees towards cloud computing. Moreover, the paper reviews the literature on ...

Using Osgeo Solutions For Local Development Systems Implementation. The Experience For The Northern Region Of Costa Rica, López-Villegas Oscar, Víquez-Acuña Oscar, Víquez-Acuña Leonardo

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

Although some general definitions classify Spatial Data Infrastructures (SDI) as technological standards, institutional and even political agreements, which allow the discovery and use of geospatial information by users for different purposes [Kuhn 2005], computationally this platforms are valuable data repositories that should reach people efficiently and effectively for analysis and decision making on issues of collective interest. Costa Rica has several SDIs experiences at national level (SNIT - http://www.snitcr.go.cr), regional level (IDEHN - http://www.idehn.tec.ac.cr) or local/cantonal level (IDESCA - http://idesca.cr). Those infrastructures can facilitate access between geospatial information managers and their ...

Kadaster Data Platform - Overview Archicture, Erwin Folmer, Wouter Beek

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

The Dutch Cadastre is publishing its geospatial data assets as Linked Open Data through the Kadaster Data Platform (KDP). The KDP supports the following three Linked Data browsing paradigms: (1) graph navigation, (2) hierarchical browsing, and (3) faceted browsing. Graph navigation uses the graph-shape of the RDF datamodel, to display concepts and instances as nodes, and properties between them as edges between those nodes. Graph navigation works well for explorative browsing. For graph navigation the KDP uses LODView (http://lodview.it), an existing OSS. Hierarchical browsing uses the tree structure of the concept hierarchy in order to display the various ...

Evaluation Of The Micro-Tasking Method For Openstreetmap Imports, Atle Frenvik Sveen, Anne Sofie Strand Erichsen

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

Open Geospatial Data, capable of enriching OpenStreetMap, is being released by governments around the world at an increasing rate. The OSM import methods have been refined since the massive TIGER-import, moving towards assisted methods such as the with micro-tasking method used by the LA and NY buildings imports. While these imports serve as great case studies of imports, they do not deal with complex datasets, or updates to the data, neither do they deal with partitioning of tasks. We examine how the Norwegian FKB-dataset can be imported to OSM using micro-tasking, and perform a user-test to determine the best partition ...

Towards A Web-Enabled Geo-Sample Web: An Open Source Resource Registration And Management System For Connecting Geo-Samples To The Web, Anusuriya Devaraju, Jens Klump, Victor Tey, Simon Cox, Ryan Fraser

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

Within the earth sciences the curation and sharing of geo-samples is crucial to supporting reproducible research, in addition to extending the use of the samples in new research, and saving costs by avoiding sample loss and duplicating sampling activities. In the Commonwealth Scientific and Industrial Research Organisation (CSIRO), researchers gather various geo-samples as part of their field studies and collaborative projects. The diversity of the samples and their unsystematic management led ambiguous sample numbers, incomplete sample descriptions, and difficulties in finding the samples and their related data. These problems are also found in universities, research institutes and government agencies, which ...

The Billion Object Platform (Bop): A System To Lower Barriers To Support Big, Streaming, Spatio-Temporal Data Sources, Devika Kakkar, Ben Lewis, David Smiley, Ariel Nunez

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a big spatio-temporal data visualization platform called the Billion Object Platform or "BOP". The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. Since once archived, streaming data gets big fast, and since most GIS systems don't support interactive visualization of millions of objects, a new platform was needed. The BOP is loaded with the latest billion geo-tweets and is fed a real-time stream of about 1 million tweets per day. The ...

Processing Conservation Indicators With Open Source Tools: Lessons Learned From The Digital Observatory For Protected Areas, Lucy Bastin, Andrea Mandrici, Luca Battistella, Grégoire Dubois

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

The European Commission has a commitment to open data and the support of open source software and standards. We present lessons learnt while populating and supporting the web and map services that underly the Joint Research Centre's Digital Observatory for Protected Areas. Challenges include: large datasets with highly complex geometries; topological inconsistencies, compounded by reprojection for equal-area calculations; multiple different representations of the same geographical entities, for example coastlines; licensing requirement to continuously update indicators to respond to monthly changes in the authoritative data. In order to compute and publish an array of indicators, we used a range of ...

Optimizing Spatiotemporal Analysis Using Multidimensional Indexing With Geowave, Richard Fecher, Michael A. Whitby

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

The open source software GeoWave bridges the gap between geographic information systems and distributed computing. This is done by preserving locality of multidimensional data when indexing it into a single-dimensional key-value store, using space filling curves. This means that like values in each dimension are stored physically close together in the datastore. We demonstrate the efficiencies and benefits of the GeoWave indexing algorithm to store and query billions of spatiotemporal data points. We show how this indexing strategy can be used to reduce query and processing times by multiple orders of magnitude using publicly available taxi trip data published by ...

Development Of An Extension Of Geoserver For Handling 3d Spatial Data, Hyung-Gyu Ryoo, Soojin Kim, Joon-Seok Kim, Ki-Joune Li

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

Recently, several open source software tools such as CesiumJS and iTowns have been developed for dealing with 3-dimensional spatial data. These tools mainly focus on visualization of 3D spatial data based on WebGL. An open-sourced server capable of storing, sharing and querying 3D spatial data has not yet been developed. GeoServer, one of the representative open source spatial data servers, provides many powerful features. In particular, it supports connecting to and publishing spatial data from a variety of data sources. GeoServer also supports Web Feature Service (WFS), which is a standard protocol established by the Open Geospatial Consortium to request ...

Self-Organized Dynamic Provisioning For Big Data, D. Cenk Erdil

D. Cenk Erdil

Recent rapid expansion of datasets in big data problems has resulted in data sizes that exceed processing capabilities of available distributed computing power. In other words, we are producing more data than we can process. In addition, further analysis of a dataset collective state may require duplicating, transferring, and distributing to increase the scale of the problem. Orchestrating these steps in large-scale complex systems is non-trivial. One basic technique to help minimize effects of data re-distribution is to use dynamic resource provisioning environments. When the node organization and structure is dynamic and eclectic, provisioning environments require up-to-date information about resource ...

Self-Organized Dynamic Provisioning For Big Data, D. Cenk Erdil

Computer Science & Information Technology Faculty Publications

Recent rapid expansion of datasets in big data problems has resulted in data sizes that exceed processing capabilities of available distributed computing power. In other words, we are producing more data than we can process. In addition, further analysis of a dataset collective state may require duplicating, transferring, and distributing to increase the scale of the problem. Orchestrating these steps in large-scale complex systems is non-trivial. One basic technique to help minimize effects of data re-distribution is to use dynamic resource provisioning environments. When the node organization and structure is dynamic and eclectic, provisioning environments require up-to-date information about resource ...

Do Your Friends Make You Buy This Brand?: Modeling Social Recommendation With Topics And Brands, Minh Duc Luu, Ee-Peng Lim

Research Collection School Of Information Systems

Consumer behavior and marketing research have shown that brand has significant influence on product reviews and product purchase decisions. However, there is very little work on incorporating brand related factors into product recommender systems. Meanwhile, the similarity in brand preference between a user and other socially connected users also affects her adoption decisions. To integrate seamlessly the individual and social brand related factors into the recommendation process, we propose a novel model called Social Brand–Item–Topic (SocBIT). As the original SocBIT model does not enforce non-negativity, which poses some difficulty in result interpretation, we also propose a non-negative version ...

Personalized Microtopic Recommendation On Microblogs, Yang Li, Jing Jiang, Ting Liu, Minghui Qiu, Xiaofei Sun

Research Collection School Of Information Systems

Microblogging services such as Sina Weibo and Twitter allow users to create tags explicitly indicated by the # symbol. In Sina Weibo, these tags are called microtopics, and in Twitter, they are called hashtags. In Sina Weibo, each microtopic has a designate page and can be directly visited or commented on. Recommending these microtopics to users based on their interests can help users efficiently acquire information. However, it is non-trivial to recommend microtopics to users to satisfy their information needs. In this article, we investigate the task of personalized microtopic recommendation, which exhibits two challenges. First, users usually do not give ...

Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal

University of New Orleans Theses and Dissertations

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a ...

Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pollution is a severe problem today, and the main challenge in water and air pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or image data. These predictions are done using machine learning. The relations between time, location, and pollution concentration are first formulated as pollution diffusion equations, which are partial differential equations (PDEs), and then deep convolutional neural networks are built and trained to solve these PDEs. The convolutional neural networks consist of convolutional layers, reLU layers ...

Pivot-Based Metric Indexing, Lu Chen, Yunjun Gao, Baihua Zheng, Christian S. Jensen, Hanyu Yang, Keyu Yang

Research Collection School Of Information Systems

The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivot-based indexing techniques for metric data has been proposed, which reduces the number of potentially expensive similarity comparisons by exploiting the triangle inequality for pruning and validation. However, no comprehensive empirical study of those techniques exists. Existing studies each offers only a narrower coverage, and they use different pivot selection strategies ...

Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi

Electronic Theses and Dissertations

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race ...