Item Type | Journal Article |
---|---|
Author | Shannon J. O’Leary |
Author | Kevin A. Feldheim |
Author | Andrew T. Fields |
Author | Lisa J. Natanson |
Author | Sabine Wintner |
Author | Nigel Hussey |
Author | Mahmood S. Shivji |
Author | Demian D. Chapman |
URL | https://academic.oup.com/jhered/article/106/3/258/2961849 |
Volume | 106 |
Issue | 3 |
Pages | 258-265 |
Publication | Journal of Heredity |
ISSN | 0022-1503 |
Date | 2015/05/01 |
Journal Abbr | J Hered |
DOI | 10.1093/jhered/esv001 |
Accessed | 2019-02-20 17:20:15 |
Library Catalog | academic.oup.com |
Language | en |
Abstract | The white shark, Carcharodon carcharias, is both one of the largest apex predators in the world and among the most heavily protected marine fish. Population gen |
Item Type | Journal Article |
---|---|
Author | Yixun Li |
Author | Mina Maleki |
Author | Nicholas J. Carruthers |
Author | Paul M. Stemmer |
Author | Alioune Ngom |
Author | Luis Rueda |
URL | https://doi.org/10.1186/s12859-018-2378-9 |
Volume | 19 |
Issue | 14 |
Pages | 410 |
Publication | BMC Bioinformatics |
ISSN | 1471-2105 |
Date | November 20, 2018 |
Journal Abbr | BMC Bioinformatics |
DOI | 10.1186/s12859-018-2378-9 |
Accessed | 2018-12-27 00:36:04 |
Library Catalog | BioMed Central |
Abstract | The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins. |
Item Type | Journal Article |
---|---|
Author | Roohollah Etemadi |
Author | Jianguo Lu |
URL | http://arxiv.org/abs/1811.01109 |
Publication | arXiv:1811.01109 [cs] |
Date | 2018-11-02 |
Extra | arXiv: 1811.01109 |
Accessed | 2018-12-26 22:41:35 |
Library Catalog | arXiv.org |
Language | en |
Abstract | Clustering coefficient is one of the most important metrics to understand the complex structure of networks. This paper addresses the estimation of clustering coefficient in network streams. There have been substantial work in this area, most of conducting empirical comparisons of various algorithms. The variance and the bias of the estimators have not been quantified. Starting with a simple yet powerful streaming algorithm, we derived the variance and bias for the estimator, and the estimators for the variances and bias. More importantly, we simplify the estimators so that it can be used in practice. The variance and bias estimators are verified extensively on 49 real networks. |
Item Type | Journal Article |
---|---|
Author | Roohollah Etemadi |
Author | Jianguo Lu |
Pages | 11 |
Library Catalog | Zotero |
Language | en |
Abstract | The number of triangles (∆) is an important metric to analyze massive graphs. It is also used to compute clustering coefficient in networks. This paper proposes a new algorithm called PES (Priority Edge Sampling) to estimate triangles in the streaming model where we need to minimize the memory window. PES combines edge sampling and reservoir sampling. Compared with the state-of-the-art streaming algorithms, PES outperforms consistently. The results are verified extensively in 48 large real-world networks in different domains and structures. The performance ratio can be as large as 21. More importantly, the ratio grows with data size almost exponentially. This is especially important in the era of big data–while we can tolerate existing algorithms for smaller datasets, our method is indispensable in very large data sampling. In addition to empirical comparisons, we also proved that the estimator is unbiased, and derived the variance. |
Item Type | Journal Article |
---|---|
Author | Yixun Li |
Author | Mina Maleki |
Author | Nicholas J. Carruthers |
Author | Paul M. Stemmer |
Author | Alioune Ngom |
Author | Luis Rueda |
URL | https://doi.org/10.1186/s12859-018-2378-9 |
Volume | 19 |
Issue | 14 |
Pages | 410 |
Publication | BMC Bioinformatics |
ISSN | 1471-2105 |
Date | November 20, 2018 |
Journal Abbr | BMC Bioinformatics |
DOI | 10.1186/s12859-018-2378-9 |
Accessed | 2018-11-24 01:33:19 |
Library Catalog | BioMed Central |
Abstract | The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins. |
Item Type | Conference Paper |
---|---|
Author | Yi Zhang |
Author | Jianguo Lu |
Author | Ofer Shai |
URL | http://dl.acm.org/citation.cfm?doid=3269206.3269320 |
Place | Torino, Italy |
Publisher | ACM Press |
Pages | 1643-1646 |
ISBN | 978-1-4503-6014-2 |
Date | 2018 |
DOI | 10.1145/3269206.3269320 |
Accessed | 2018-10-28 00:51:20 |
Library Catalog | Crossref |
Conference Name | the 27th ACM International Conference |
Language | en |
Abstract | Learning network representations is essential for many downstream tasks such as node classification, link prediction, and recommendation. Many algorithms derived from SGNS (skip-gram with negative sampling) have been proposed, such as LINE, DeepWalk, and node2vec. In this paper, we show that these algorithms suffer from norm convergence problem, and propose to use L2 regularization to rectify the problem. The proposed method improves the embeddings consistently. This is verified on seven different datasets with various sizes and structures. The best improvement is 46.41% for the task of node classification. |
Proceedings Title | Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18 |
Item Type | Attachment |
---|---|
URL | https://f1000researchdata.s3.amazonaws.com/manuscripts/10141/cb25072e-28b1-439b-a2f8-8e8979db1aec_9417_-_peter_rogan.pdf?doi=10.12688/f1000research.9417.1 |
Accessed | 2018-09-16 02:34:00 |
Link Mode | 1 |
MIME Type | application/pdf |
Item Type | Conference Paper |
---|---|
Author | Ying Xiao |
Author | C. I. Ezeife |
Editor | Carlos Ordonez |
Editor | Ladjel Bellatreche |
Series | Lecture Notes in Computer Science |
Publisher | Springer International Publishing |
Pages | 70-82 |
ISBN | 978-3-319-98539-8 |
Date | 2018 |
Library Catalog | Springer Link |
Language | en |
Abstract | In E-commerce, user-item rating matrices for collaborative filtering recommendation systems are usually binary and sparse, showing only whether or not a user has purchased an item previously. Clickstream data containing more customer behavior have been used to improve recommendations by some existing systems referred in this paper as Kim05Rec, Kim11Rec, and Chen13Rec, using decision tree, association rule mining and category-based interest measurements respectively. However, they do not integrate valuable information from historical purchases and the consequential bond information between session-based clicks and purchases. This paper proposes Historical Purchase with Clickstream recommendation system (HPCRec), which normalizes the historical purchase frequency matrix to improve rating quality, and mines the session-based consequential bond between clicks and purchases to generate potential ratings to improve the rating quantity. Experimental results show HPCRec outperforms these existing methods, and is also capable of handling infrequent user cases, whereas other methods can not. |
Proceedings Title | Big Data Analytics and Knowledge Discovery |
Item Type | Book Section |
---|---|
Author | Ryan Scott |
Author | Brian MacPherson |
Author | Robin Gras |
Editor | Maria Isabel Aldinhas Ferreira |
Editor | João Silva Sequeira |
Editor | Rodrigo Ventura |
URL | https://doi.org/10.1007/978-3-319-97550-4_14 |
Series | Intelligent Systems, Control and Automation: Science and Engineering |
Place | Cham |
Publisher | Springer International Publishing |
Pages | 223-278 |
ISBN | 978-3-319-97550-4 |
Date | 2019 |
Extra | DOI: 10.1007/978-3-319-97550-4_14 |
Accessed | 2018-09-16 01:22:53 |
Library Catalog | Springer Link |
Language | en |
Abstract | This chapter discusses individual-based models (IBMs) and uses the Overview, Design concepts, and Details (ODD) protocol to describe a predator-prey evolutionary ecosystem IBM called EcoSim. EcoSim is one of the most complex and large-scale IBMs of its kind, allowing hundreds of thousands of intricate individuals to interact and evolve over thousands of time steps. Individuals in EcoSim have a behavioral model represented by a fuzzy cognitive map (FCM). The FCM, described in this chapter, is a cognitive architecture well-suited for individuals in EcoSim due to its efficiency and the complexity of decision-making it allows. Furthermore, it can be encoded as a vector of real numbers, lending itself to being part of the genetic material passed on by individuals during reproduction. This allows for meaningful evolution of their behaviors and natural selection without predefined fitness. EcoSim has been enhanced to increase the breadth and depth of the questions it can answer. New features include: fertilization of primary producers by consumers, predator-prey combat, sexual reproduction, sex-linkage of genes, multiple modes of reproduction, size-based dominance hierarchy, and more. In addition to describing EcoSim in detail, we present data from default EcoSim runs to show potential users the types of data EcoSim generates. Furthermore, we present a brief sensitivity analysis of some variables in EcoSim, and a case study that demonstrates research that can be performed using EcoSim. In the case study, we elucidate some evolutionary and behavioral impacts on animals under two conditions: when primary production is limited, and when energy expenditure is reduced. |
Book Title | Cognitive Architectures |
Short Title | EcoSim, an Enhanced Artificial Ecosystem |
Item Type | Journal Article |
---|---|
Author | Kalyani Selvarajah |
Author | Pooya Moradian Zadeh |
Author | Ziad Kobti |
Author | Mehdi Kargar |
Author | Mohd Tazim Ishraque |
Author | Kathryn Pfaff |
Pages | 7 |
Library Catalog | Zotero |
Language | en |
Abstract | In this paper, a novel knowledge-based evolutionary algorithm is proposed to assemble a team of care providers for patients in community-oriented palliative care. The main objective of this research is to optimize the patient’s care services and human resource allocation process. From a system perspective in palliative care, there exists a group of patients with needs who are not able to perform some of their ordinary life activities due to their limited capability, as a consequence of their disease or disorders. On the other hand, we have a group of care providers who are capable, skilled, and ready to provide a wide range of services to the patients to fulfill those needs. This poses the challenge of assigning members to a team of care providers in an optimal manner to help the patient satisfy their needs, while taking into consideration the communication, distance and contact costs. To deal with this problem, we propose a novel algorithm based on a cultural algorithm (CA) as the basis for our model for assembling an optimal team of care providers. The overall goals are to minimize the costs and increase the patient’s satisfaction rate. We have evaluated our model using multiple synthetic networks and conducted comparative analysis with other existing methods. The results show that our proposed model can overcome the shortcomings posed by the existing approaches. |
Item Type | Journal Article |
---|---|
Author | Ashraf Abou Tabl |
Author | Abedalrhman Alkhateeb |
Author | Huy Quang Pham |
Author | Luis Rueda |
Author | Waguih ElMaraghy |
Author | Alioune Ngom |
URL | https://doi.org/10.1177/1176934318790266 |
Volume | 14 |
Pages | 1176934318790266 |
Publication | Evolutionary Bioinformatics |
ISSN | 1176-9343 |
Date | January 1, 2018 |
Journal Abbr | Evol Bioinform Online |
DOI | 10.1177/1176934318790266 |
Accessed | 2018-08-23 02:49:28 |
Library Catalog | SAGE Journals |
Language | en |
Abstract | Analyzing the genetic activity of breast cancer survival for a specific type of therapy provides a better understanding of the body response to the treatment and helps select the best course of action and while leading to the design of drugs based on gene activity. In this work, we use supervised and nonsupervised machine learning methods to deal with a multiclass classification problem in which we label the samples based on the combination of the 5-year survivability and treatment; we focus on hormone therapy, radiotherapy, and surgery. The proposed nonsupervised hierarchical models are created to find the highest separability between combinations of the classes. The supervised model consists of a combination of feature selection techniques and efficient classifiers used to find a potential set of biomarker genes specific to response to therapy. The results show that different models achieve different performance scores with accuracies ranging from 80.9% to 100%. We have investigated the roles of many biomarkers through the literature and found that some of the discriminative genes in the computational model such as ZC3H11A, VAX2, MAF1, and ZFP91 are related to breast cancer and other types of cancer. |
Item Type | Journal Article |
---|---|
Author | Nima Moradianzadeh |
Author | Pooya Moradian Zadeh |
Author | Ziad Kobti |
Author | Sarah Hansen |
Author | Kathryn Pfaff |
URL | http://www.sciencedirect.com/science/article/pii/S1084804518302285 |
Volume | 120 |
Pages | 30-41 |
Publication | Journal of Network and Computer Applications |
ISSN | 1084-8045 |
Date | October 15, 2018 |
Journal Abbr | Journal of Network and Computer Applications |
DOI | 10.1016/j.jnca.2018.07.004 |
Accessed | 2018-07-23 12:23:59 |
Library Catalog | ScienceDirect |
Abstract | Palliative and end-of-life care are special types of healthcare that focus on improving the quality of life of patients who are living with life-threatening illness or nearing their end of life. The primary goal here is to provide various support services to help the patients to maintain an active life and dignity. Assuming there are cost and resource limitations for delivering care within the system, where each care provider can support a limited number of patients, the problem can be defined as finding a set of suitable care providers with a minimum overall cost to match the needs of the maximum number of patients. In the grand scheme, the whole care system can be seen as a social network consisting of patients and care providers. This representation provides an opportunity to apply social network analysis techniques to enhance the topology of the system and improve its efficiency. In this paper, we propose a novel computational agent-based model to address this problem by extending the agent's capabilities using the benefits of the social network. We assume that each patient agent can cover its disabilities and perform its desired tasks through collaboration with other agents. The primary objective is to optimize a dynamic, personalized care pathway system that will support palliative care within a community eco-system. Testing the ability of the system to match social support agents with personal preferences, needs, and capabilities is the second goal of this research work. In addition, we are going to measure the impact of the system on perceived quality of life, social connectedness, caregiver burden, and care satisfaction. The performance and functionality of our proposed model have been evaluated using various synthetic and a real palliative networks. The results demonstrate a significant reduction in the operational costs and enhancement of the service quality. |
Item Type | Conference Paper |
---|---|
Author | H. R. Rajpura |
Author | A. Ngom |
Pages | 1-7 |
Date | May 2018 |
DOI | 10.1109/CIBCB.2018.8404972 |
Library Catalog | IEEE Xplore |
Conference Name | 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Abstract | Predicting new drug target interactions experimentally through wet lab experiments is time as well as resource intensive. In general, drug-target interaction prediction problem leads to drug discovery, drug repositioning and uncovers interesting patterns in chemogenomics research. Drug and target represent heterogeneous nodes within a network of interactions. Presence of an edge between the nodes indicates a positive interaction whereas an absence suggests an unknown interaction. Classification based machine learning algorithms are heavily applied in this area of research. Classification algorithms need positive as well as negative data to yield optimized results. The major problem in this field is lack of negative data because the data that are found in the public databases are positive interaction samples. Considering unknown drug target pairs as negative data may cause severe consequences for the prediction performance. Thereby, we propose a positive un-labelled (PU) learning- based approach that uses one class support vector machine technique as the learning algorithm. The algorithm learns the positive distribution from the unified feature vector space of drugs and targets and regards unknown pairs as unlabeled instead of labelling them as negative pairs. Additionally, we use 4860 Klekota Roth fingerprint + 881 PubChem fingerprint as a high dimensional and highly discriminative feature vector representation for drugs. To represent protein features, we create a protein-motif matrix based on the sliding window score that records the probability of a motif pattern occurring within a given protein sequence. Also, we separately evaluate the prediction performance using 5-fold nested cross- validation under different experimental setting for each of the four formulations: 1) Known drug-target pair,2) Drug prediction, 3) Target prediction and 4) Unknown drug target pair. We show that our approach yields the best AUC score over previous benchmark techniques and outperf- rms most of the recent works based on one class classifiers and PU-based learning. |
Proceedings Title | 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Item Type | Conference Paper |
---|---|
Author | Udayamoorthy Navaneetha Krishnan |
Author | Md Zamilur Rahman |
Author | Asish Mukhopadhyay |
Author | Yash P. Aneja |
URL | http://link.springer.com/chapter/10.1007/978-3-319-95165-2_29 |
Series | Lecture Notes in Computer Science |
Publisher | Springer, Cham |
Pages | 412-424 |
ISBN | 978-3-319-95164-5 978-3-319-95165-2 |
Date | 2018/05/02 |
DOI | 10.1007/978-3-319-95165-2_29 |
Accessed | 2018-07-10 12:17:59 |
Library Catalog | link-springer-com.ledproxy2.uwindsor.ca |
Conference Name | International Conference on Computational Science and Its Applications |
Language | en |
Abstract | The Molecular Distance Geometry Problem (MDGP) is defined as the determination of the three-dimensional structure of a molecule using a subset of interatomic distances available. This is a special case of the problem of determining if a weighted graph can be embedded in a k-dimensional Euclidean space such the edge-weights are equal to the Euclidean distances between the corresponding embedded vertices. In the Phillips’ model, a molecule is viewed as a chain of atoms, with a fixed bond length between two successive atoms, and the bond angle formed by three successive atoms also set to a fixed value. If i,i+1,i+2i,i+1,i+2i, i+1, i+2 and i+3i+3i+3 are the indices of four successive atoms, the torsion angle is the dihedral angle between the planes formed by the atoms i,i+1,i+2i,i+1,i+2i, i+1, i+2 and the atoms i+1,i+2,i+3i+1,i+2,i+3i+1, i+2, i+3. This angle is randomly chosen from a well-defined set. These choices fix the coordinates of the atoms, up to a rigid motion. In this paper, we propose a Degree of Freedom (DoF) approach to construct the atomic coordinates of a molecule in the framework of Phillips’ model. In the DoF approach, we exploit the fact that n atoms in 3-space have 3n degrees of freedom, and each distance constraint reduces the degree of freedom by 1. In this approach, the torsion angles are not set. Instead, we exploit the fact if the distances from an atom with index i to atoms with indices i−1i−1i-1 and i−2i−2i-2 are known then the distance graph is chordal, allowing us to apply the Distance Matrix Completion Algorithm (DMCA) due to Zamilur et al. [1] to complete the remaining distances. Finally, the Stochastic Proximity Embedding (SPE) heuristic due to Agrafoitis [2] is used to determine the atomic coordinates. |
Proceedings Title | Computational Science and Its Applications – ICCSA 2018 |
Short Title | Molecular Structure Determination in the Phillips’ Model |
Item Type | Conference Paper |
---|---|
Author | Jianguo Lu |
Author | Hao Wang |
Author | Dingding Li |
URL | https://doi.org/10.1145/3184558.3186240 |
Series | WWW '18 |
Place | Republic and Canton of Geneva, Switzerland |
Publisher | International World Wide Web Conferences Steering Committee |
Pages | 495–499 |
ISBN | 978-1-4503-5640-4 |
Date | 2018 |
DOI | 10.1145/3184558.3186240 |
Accessed | 2018-04-24 16:33:43 |
Library Catalog | ACM Digital Library |
Abstract | We show that uniform random sampling is not as effective as PPS (probability proportional to size) sampling in many estimation tasks. In the setting of (graph) size estimation, this paper demonstrates that random edge sampling outperforms random node sampling, with a performance ratio proportional to the normalized graph degree variance. This result is particularly important in the era of big data, when data are typically large and scale-free, resulting in large degree variance. We derive the result by first giving the variances of random node and random edge estimators. A simpler and more intuitive result is obtained by assuming that the data is large and degree distribution follows a power law. |
Proceedings Title | Companion of the The Web Conference 2018 on The Web Conference 2018 |
Item Type | Conference Paper |
---|---|
Author | Osama Hamzeh |
Author | Abedalrhman Alkhateeb |
Author | Luis Rueda |
URL | https://link.springer.com/chapter/10.1007/978-3-319-78723-7_29 |
Series | Lecture Notes in Computer Science |
Publisher | Springer, Cham |
Pages | 343-351 |
ISBN | 978-3-319-78722-0 978-3-319-78723-7 |
Date | 2018/04/25 |
DOI | 10.1007/978-3-319-78723-7_29 |
Accessed | 2018-04-24 16:22:13 |
Library Catalog | link.springer.com |
Conference Name | International Conference on Bioinformatics and Biomedical Engineering |
Language | en |
Abstract | Prostate cancer can be missed due to the limited number of biopsies or the ineffectiveness of standard screening methods. Finding gene biomarkers for prostate cancer location and analyzing their transcriptomics can help clinically understand the development of the disease and improve treatment efficiency. In this work, a classification model is built based on gene expression measurements of samples from patients who have cancer on the left, right, and both lobes of the prostate as classes.A hybrid feature selection is used to select the best possible set of genes that can differentiate the three classes. Standard machine learning classifiers with the one-versus-all technique are used to select potential biomarkers for each laterality class. RNA-sequencing data from The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) was used. This dataset consists of 450 samples from different patients with different cancer locations. There are three primary locations within the prostate: left, right and bilateral. Each sample in the dataset contains expression levels for each of the 60,488 genes; the genes are expressed in Transcripts Per Kilobase Million (TPM) values.The results show promising prediction prospect for prostate cancer laterality. With 99% accuracy, a support vector machine (SVM) based on a radial basis function kernel (SVM-RBF) was able to identify each group from the others using the subset of genes. Three groups of genes (RTN1, HLA-DMB, MRI1 and others) were found to be differentially expressed among the three different tumor locations. The findings were validated using multiple findings in the literature, which confirms the relationship between those genes and prostate cancer. |
Proceedings Title | Bioinformatics and Biomedical Engineering |
Item Type | Journal Article |
---|---|
Author | Jianguo Lu |
Author | Hao Wang |
URL | http://www.sciencedirect.com/science/article/pii/S0020025516307708 |
Publication | Information Sciences |
ISSN | 0020-0255 |
Date | August 16, 2017 |
Journal Abbr | Information Sciences |
DOI | 10.1016/j.ins.2017.08.030 |
Accessed | 2017-08-25 14:33:59 |
Library Catalog | ScienceDirect |
Abstract | The norm of data size estimation is to use uniform random samples whenever possible. There have been tremendous efforts in obtaining uniform random samples using methods such as Metropolis-Hasting random walk or importance sampling [2]. This paper shows that, on the contrary to the common practice, uniform random sampling should be avoided when PPS (probability proportional to size) sampling is available for large data. To develop intuition of the sampling process, we discuss the sampling and estimation problem in the context of graph. The size is the number of nodes in the graph; uniform random sampling corresponds to uniform random node (RN) sampling; and PPS sampling is approximated by random edge (RE) sampling. In this setting, we show that for large graphs RE sampling outperforms RN sampling with a ratio proportional to the normalized graph degree variance. This result is particularly important in the era of big data, when data are typically large and scale-free [3], resulting in large degree variance. We derive the result by giving the variances of RN and RE estimators. Each step of the derivation is supported and demonstrated by simulation studies assuming power law distributions. Then we use 18 real-world networks to verify the result. Furthermore, we show that the performance of random walk (RW) sampling is data dependent and can be significantly worse than RN and RE. More specifically, RW can estimate online social networks but not Web graphs due to the difference of the graph conductance. |
Item Type | Book Section |
---|---|
Author | C. I. Ezeife |
URL | https://link.springer.com/chapter/10.1007/978-1-4615-5915-3_17 |
Publisher | Springer, Boston, MA |
Pages | 195-211 |
ISBN | 978-1-4613-7712-2 978-1-4615-5915-3 |
Date | 1997 |
Extra | DOI: 10.1007/978-1-4615-5915-3_17 |
Accessed | 2018-01-11 15:25:49 |
Library Catalog | link-springer-com.ledproxy2.uwindsor.ca |
Language | en |
Abstract | A data warehouse is a large database integrating data from a number of enterprise independent source application databases over a long period of time [8]. A data warehouse is organized around major subjects (entities) of an enterprise and not around its functions. For example, in a banking environment which has two independent source databases, one for savings account functions and the other for chequing account functions, a data warehouse that integrates these two databases has the following basic structure: bt (customised (C), accounttype (A), time (T), balance). |
Book Title | Systems Development Methods for the Next Century |
Item Type | Conference Paper |
---|---|
Author | Christie I. Ezeife |
Author | Min Chen |
URL | https://link.springer.com/chapter/10.1007/978-3-540-27772-9_54 |
Series | Lecture Notes in Computer Science |
Publisher | Springer, Berlin, Heidelberg |
Pages | 539-548 |
ISBN | 978-3-540-22418-1 978-3-540-27772-9 |
Date | 2004/7/15 |
DOI | 10.1007/978-3-540-27772-9_54 |
Accessed | 2018-01-11 15:07:38 |
Library Catalog | link-springer-com.ledproxy2.uwindsor.ca |
Conference Name | International Conference on Web-Age Information Management |
Language | en |
Abstract | Since point and click at web pages generate continuous data stream, which flow into web log data, old patterns may be stale and need to be updated. Algorithms for mining web sequential patterns from scratch include WAP, PLWAP and apriori-based GSP. An incremental technique for updating already mined patterns when database changes, which is based on an efficient sequential mining technique like the PLWAP is needed.This paper proposes an algorithm, Re-PL4UP, which uses the PLWAP tree structure to incrementally update web sequential patterns. Re-PL4UP scans only the new changes to the database, revises the old PLWAP tree to accommodate previous small items that have become large and previous large items that have become small in the updated database without the need to scan the old database. The approach leads to improved performance. |
Proceedings Title | Advances in Web-Age Information Management |
Item Type | Book Section |
---|---|
Author | C. I. Ezeife |
Author | Jian Zheng |
URL | https://link.springer.com/chapter/10.1007/978-1-4615-4261-2_3 |
Publisher | Springer, Boston, MA |
Pages | 51-60 |
ISBN | 978-1-4613-6913-4 978-1-4615-4261-2 |
Date | 1999 |
Extra | DOI: 10.1007/978-1-4615-4261-2_3 |
Accessed | 2018-01-11 14:49:38 |
Library Catalog | link-springer-com.ledproxy2.uwindsor.ca |
Language | en |
Abstract | Applying an object based horizontal fragmentation scheme to an object oriented database system, creates subsets of class extents which are allocated to sites where they are most needed. As new class instances are created or database schema evolves or application queries access patterns change at distributed sites, system performance may drop. Restoring the system performance requires re-running the object horizontal fragmentation scheme after conducting a full static system requirements analysis. This paper proposes an object horizontal distributed design architecture which determines system performance threshold, monitors changes in distributed design input which may affect performance and dynamically triggers a re-fragmentation of the system if performance drops below the system performance threshold. |
Book Title | Systems Development Methods for Databases, Enterprise Modeling, and Workflow Management |
Item Type | Conference Paper |
---|---|
Author | Christie I. EZEIFE |
Author | Yi Liu |
Author | Chungsheng Liu |
Author | Ingo Schmitt |
Place | San Juan, Puerto Rico |
Date | January, 2002 |
Conference Name | NSF/NSERC Conference |
Item Type | Conference Paper |
---|---|
Author | Ritu Chaturvedi |
Author | Dragana Martinovic |
Author | Christiana I. Ezeife |
Place | Ryerson University, Toronto, Canada |
Date | July, 2015 |
Conference Name | International Social Sciences and Education Research Conference: ICBT |
Item Type | Book |
---|---|
Author | Lichun Zhu |
Author | C I Ezeife |
Author | Robert Kent |
Date | January 1, 2007 |
Abstract | Construction of flexible query interface constitutes a very important part in the design of information systems. The major goal is that new queries can easily be built by either the developers or the end-users of information systems. Some information systems would provide a list of predefined queries and future additional queries would need to be reconstructed from scratch. Thus, the low degree of reusability of query modules is a limitation of the database query report systems that these information systems are based on. This paper presents Generic Query Toolkit, a software package that automates the query interface generation process. It consists of a parser and an interpreter for a newly defined Generic Query Script Language, a background query processing unit, a presentation layer service provider and the presentation layer component. Data mining querying feature has been integrated into this query language. Future work will integrate more data mining querying and other advanced features. |
Item Type | Conference Paper |
---|---|
Author | Lichun Zhu |
Author | C. I. Ezeife |
Author | R. D. Kent |
Place | Vancouver B.C., Canada |
Date | May, 2007 |
Library Catalog | Google Scholar |
Proceedings Title | Information Resources Management Association (IRMA) |
Short Title | Generic query toolkit |
Item Type | Journal Article |
---|---|
Author | Lichun Zhu |
Author | C. I. Ezeife |
Author | R. D. Kent |
Publication | Information Resources Management Association (IRMA), Vancouver |
Date | 2007 |
Library Catalog | Google Scholar |
Short Title | Generic query toolkit |