Open Access at Cardiff University - Sonja Haerkoenen
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Implementing open access in a research institution (Cardiff University) presented at CLIC Open Access event 24th July 2014 by Sonja Haerkoenen #CLICOA
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Open Access to Welsh Government publications and information - presented at CLIC Open Access event on 24th July 2014
Community membership at Cardiff University Libraries - Sally Earney
Community membership of Cardiff University Libraries presented at CLIC Open Access event on 24th July 2014 by Sally Earney #CLICOA
Data mining for prediction of human
The recruitment of new personnel is one of the most essential business processes which affect the quality of
human capital within any company. It is highly essential for the companies to ensure the recruitment of
right talent to maintain a competitive edge over the others in the market. However IT companies often face
a problem while recruiting new people for their ongoing projects due to lack of a proper framework that
defines a criteria for the selection process. In this paper we aim to develop a framework that would allow
any project manager to take the right decision for selecting new talent by correlating performance
parameters with the other domain-specific attributes of the candidates. Also, another important motivation
behind this project is to check the validity of the selection procedure often followed by various big
companies in both public and private sectors which focus only on academic scores, GPA/grades of students
from colleges and other academic backgrounds. We test if such a decision will produce optimal results in
the industry or is there a need for change that offers a more holistic approach to recruitment of new talent
in the software companies. The scope of this work extends beyond the IT domain and a similar procedure
can be adopted to develop a recruitment framework in other fields as well. Data-mining techniques provide
useful information from the historical projects depending on which the hiring-manager can make decisions
for recruiting high-quality workforce. This study aims to bridge this hiatus by developing a data-mining
framework based on an ensemble-learning technique to refocus on the criteria for personnel selection. The
results from this research clearly demonstrated that there is a need to refocus on the selection-criteria for
quality objectives.
Recommending tags for new resources
Social bookmarking system is a web-based resource sharing system that allows users to upload, share and
organize their resources i.e. bookmarks and publications. The system has shifted the paradigm of
bookmarking from an individual activity limited to desktop to a collective activity on the web. It also
facilitates user to annotate his resource with free form tags that leads to large communities of users to
collaboratively create accessible repositories of web resources. Tagging process has its own challenges
like ambiguity, redundancy or misspelled tags and sometimes user tends to avoid it as he has to describe
tag at his own. The resultant tag space is noisy or very sparse and dilutes the purpose of tagging. The
effective solution is Tag Recommendation System that automatically suggests appropriate set of tags to
user while annotating resource. In this paper, we propose a framework that does not depend on tagging
history of the resource or user and thereby capable of suggesting tags to the resources which are being
submitted to the system first time. We model tag recommendation task as multi-label text classification
problem and use Naive Bayes classifier as the base learner of the multilabel classifier. We experiment with
Boolean, bag-of-words and term frequency-inverse document frequency (TFIDF) representation of the
resources and fit appropriate distribution to the data based on the representation used. Impact of feature
selection on the effectiveness of the tag recommendation is also studied. Effectiveness of the proposed
framework is evaluated through precision, recall and f-measure metrics.
PATTERN GENERATION FOR COMPLEX DATA USING HYBRID MINING
This document discusses a hybrid data mining approach called combined mining that can generate informative patterns from complex data sources. It proposes applying three techniques: 1) Using the Lossy-counting algorithm on individual data sources to obtain frequent itemsets, 2) Generating incremental pair and cluster patterns using a multi-feature approach, 3) Combining FP-growth and Bayesian Belief Network using a multi-method approach to generate classifiers. The approach is tested on two datasets to obtain more useful knowledge and the results are compared.
IDENTIFICATION OF OUTLIERS IN OXAZOLINES AND OXAZOLES HIGH DIMENSION MOLECULA...
This document summarizes an algorithm called Principal Component Outlier Detection (PrCmpOut) for identifying outliers in high-dimensional molecular descriptor datasets. PrCmpOut uses principal component analysis to transform the data into a lower-dimensional space, where it can more efficiently detect outliers using robust estimators of location and covariance. The properties of PrCmpOut are analyzed and compared to other robust outlier detection methods through simulation studies using a dataset of oxazoline and oxazole molecular descriptors. Numerical results show PrCmpOut performs well at outlier detection in high-dimensional data.
USING GOOGLE’S KEYWORD RELATION IN MULTIDOMAIN DOCUMENT CLASSIFICATION
The document describes a new method for multi-domain document classification using keyword sequences extracted from documents. It introduces the Word AdHoc Network (WANET) system which uses Google's Keyword Relation and a new similarity measurement called Google Purity to classify documents into domains based on their extracted 4-word keyword sequences, without requiring pre-established keyword repositories. Experimental results showed the classification was accurate and efficient, allowing cross-domain classification and management of knowledge from different sources.
Data performance characterization of frequent pattern mining algorithms
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle extremely
huge amount of data, it is quite natural that the demand for the computational environment to accelerates,
and scales out big data applications increases. The important thing is, however, the behavior of big data
applications is not clearly defined yet. Among big data applications, this paper specifically focuses on stream mining applications. The behavior of stream mining applications varies according to the characteristics of the input data. The parameters for data characterization are, however, not clearly defined yet, and there is no study investigating explicit relationships between the input data, and streammining applications, either. Therefore, this paper picks up frequent pattern mining as one of the
representative stream mining applications, and interprets the relationships between the characteristics of the input data, and behaviors of signature algorithms for frequent pattern mining.
Real time data processing frameworks
On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being co-relational to Hadoop is false; focus needs to be given on business value as well. Data Warehousing, Hadoop and stream processing complement each other very well. In this paper, we have tried reviewing a few frameworks and products
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Column store databases approaches and optimization techniques
Column-Stores database stores data column-by-column. The need for Column-Stores database arose for
the efficient query processing in read-intensive relational databases. Also, for read-intensive relational
databases,extensive research has performed for efficient data storage and query processing. This paper
gives an overview of storage and performance optimization techniques used in Column-Stores.
Building a collective experience
This paper describes the Collective Experience Engine (CEE), a system for indexing Experiential-
Knowledge about Web knowledge-sources (websites), and performing relative-experience calculations
between participants of the CEE. The CEE provides an in-browser interface to query the collective
experience of others participating in the CEE. This interface accepts a list of URLs, to which the CEE adds
additional information based on the Queryee's previously indexed Experiential-Knowledge. The core of the
CEE is its Experiential-Context Conversation (ECConversation) functionality, whereby an collection of a
person’s Web Experiential-Knowledge can be utilized to allow a real-world conversation-like exchange of
information to take place, including adjusting information-flow based on the Queryee's experiential
background and knowledge, and providing additional experientially-related knowledge integrated into the
answer from multiple selected 'experience donors'. A relative-experience calculation ensures that
information is retrieved only from relative-experts, to ensure sufficient additional information exists, but
that such information isn't too advanced for the Queryee to process. This paper gives an overview of the
CEE, and the underlying algorithms and data structures, and describes a system architecture and
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A simplified approach for quality management in data warehouse
Data warehousing is continuously gaining importance as organizations are realizing the benefits of
decision oriented data bases. However, the stumbling block to this rapid development is data quality issues
at various stages of data warehousing. Quality can be defined as a measure of excellence or a state free
from defects. Users appreciate quality products and available literature suggests that many organization`s
have significant data quality problems that have substantial social and economic impacts. A metadata
based quality system is introduced to manage quality of data in data warehouse. The approach is used to
analyze the quality of data warehouse system by checking the expected value of quality parameters with
that of actual values. The proposed approach is supported with a metadata framework that can store
additional information to analyze the quality parameters, whenever required.
Stratification of clinical survey data
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study population consists of subpopulations that are of particular interest. In clinical data, once the data is
stratified into sub populations based on a significant stratifying factor, different risk factors can be
determined from each subpopulation. In this paper, the Fisher’s Exact Test is used to determine the
significant stratifying factors. The experiments are conducted on a simulated study and the Medical,
Epidemiological and Social Aspects of Aging (MESA) data constructed for prediction of urinary
incontinence. Results show that, smoking is the most significant stratifying factor of MESA data, showing
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PATTERN GENERATION FOR COMPLEX DATA USING HYBRID MINING
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- 1. “Implementing Open Access in a research institution – an eye-opening experience” Sonja Haerkoenen
- 2. OA Terms Funders & OA OA on the ground
- 3. GREEN Open Access – self-archiving Publish in a conventional journal, but provide author post-print in OA repository (e.g. ORCA) Articles peer-reviewed Publishers often apply embargo Publisher’s version often only available to subscribers
- 4. GOLD Open Access - publishing Publish in an OA journal (e.g. BioMed Central, PLOS) and pay an author fee Articles peer-reviewed Articles available to anyone to read without further charge
- 5. Hybrid Open Access - publishing Publish in a conventional journal Simultaneously or retrospectively pay an author fee to have article made openly available, without subscription Publisher receives both library subscriptions plus author fees
- 6. Creative Commons = CC BY= Attribution NC= NonCommercial ND= NoDerivatives SA= ShareAlike http://creativecommons.org/ licenses/ Options vary by journal CC-BY not always available OA Licences
- 8. International Drive towards Open Access
- 9. OA Liaison Team Finance School Research Offices Research, Innovation & Enterprise Services Subject Librarians Open Access Allocation Group ORCA Team Academics
- 11. OpenAccess@cardiff.ac.uk 130 Gold articles Wellcome Trust fund 100% OA RCUK OA grant for OA @ Cardiff for the first 5 years 44 OA sessions Increase from 7% to 15% full text in ORCA
- 12. Discipline differences Doesn’t only affect staff but also PhD students Different funders – different requirements Broad strategies – we have to deal with issues on case by case basis C0ncerns about licences, third party materials, other output types (books, chapters, compositions…) Publisher liaison time-consuming Lots of checking….. and double-checking Raising awareness