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Article
Publication date: 11 July 2016

Lauren Mandel

The purpose of this paper is to share the research processes and results of secondary analysis using GIS to map usage of a university library to contribute to ongoing…

1101

Abstract

Purpose

The purpose of this paper is to share the research processes and results of secondary analysis using GIS to map usage of a university library to contribute to ongoing efforts to help identify how library spaces are used to explain how university libraries can continue to evolve as teaching, learning, and shared communities of scholars. This paper details the use of ArcGIS to visualize where students are in the library in order to explain how this method can used by libraries to visualize the use of their facilities.

Design/methodology/approach

This research utilized secondary analysis of data collected during seating sweeps; through secondary analysis, data were analyzed and visualized in ArcGIS. The seating sweeps were conducted three times a day during a sample week, with researchers noting on maps of the library floor plan where students were sitting. Data were entered into an ArcGIS database file and mapped to display usage directly on the library map to improve stakeholders’ understanding of the ways students are using the library as a place.

Findings

Even though this project used consistent instruments and procedural instructions and trained observers, a combination of factors resulted in an incomplete data set, including the length of time between research design and data collection and lack of agreement about the use of map worksheets. It was still possible to make maps that depict heavier and lighter areas of use, present data to library stakeholders, and show what can be accomplished when data are collected on copies of the floor plan.

Research limitations/implications

This research is limited by being a conducted in one university library, but the implications far outweigh the limitations. While bar and pie charts are effective at visualizing data, they do not provide a way to visualize where activities occur; maps provide multi-layered visualization, allowing libraries to visualize the same usage data as bar, pie, or other charts in addition to seeing where that usage occurs. The implications for librarianship include better understanding of how library spaces are used and the ability to use visually appealing maps to demonstrate the library’s use, value, and impact.

Originality/value

Mapping library statistics is an area that has been growing in the last decade, but practical examples of using GIS to map facility usage are few. This paper explains in detail how the mapping process works and how libraries of all types can adapt this method for their own usage assessments to more vividly depict the value and impact of the library facility as a place.

Details

Performance Measurement and Metrics, vol. 17 no. 2
Type: Research Article
ISSN: 1467-8047

Keywords

Content available

Abstract

Details

Journal of Educational Administration, vol. 55 no. 4
Type: Research Article
ISSN: 0957-8234

Article
Publication date: 15 February 2008

Richard S. Segall, Gauri S. Guha and Sarath A. Nonis

This paper seeks to present a complete set of graphical and numerical outputs of data mining performed for microarray databases of plant data as described in earlier…

Abstract

Purpose

This paper seeks to present a complete set of graphical and numerical outputs of data mining performed for microarray databases of plant data as described in earlier research by the authors. A brief description of data mining is also presented, as well as a brief background of previous research.

Design/methodology/approach

The paper uses applications of data mining using SAS Enterprise Miner Version 4 for plant data from the Osmotic Stress Microarray Information Database (OSMID) that is available on the web for both normalized and log(2) transformed data.

Findings

This paper illustrates that useful information about the effects of environmental stress tolerances (ESTs) on plants can be obtained by using data mining.

Research limitations/implications

Use of SAS Enterprise Miner was very effective for performing data mining of microarray databases with its modules of cluster analysis, decision trees, and descriptive and visual statistics.

Practical implications

The data used from the OSMID database are considered to be representative of those that could be used for biotech application such as the manufacture of plant‐made‐pharmaceuticals and genetically modified foods.

Originality/value

This paper contributes to the discussion on the use of data mining for microarray databases and specifically for studying the effects of ESTs on plants.

Details

Kybernetes, vol. 37 no. 1
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 20 February 2009

Aristeidis Meletiou and Anthi Katsirikou

This paper aims to describe a data analysis methodology using data‐ and knowledge‐mining techniques focused on libraries. It concerns data analysis techniques in general…

1228

Abstract

Purpose

This paper aims to describe a data analysis methodology using data‐ and knowledge‐mining techniques focused on libraries. It concerns data analysis techniques in general, as well as ways in which they could be applied to library management. The ultimate purpose of this data process is to make the exported information useful to decision makers, so as to help them with decision making and strategy planning. This will lead to a more efficient organisation of the internal processing, and to improvement of the services offered in a library.

Design/methodology/approach

Methodologies based on knowledge and data mining are used to analyse the real data in one specific case study library (Library of Technical University of Crete, Greece) in order to describe the concept better. The results obtained concern the extraction of information about the inter‐relations of data and the definition of factors that can be used in library management and strategic planning. The scope of the paper is to show how data coming from libraries can be analysed to give useful results for decision‐makers, in order to improve the services they offer.

Findings

The paper provides a detailed list of all existing data resources in a library and describes step‐by‐step an analysis methodology based on processes of knowledge discovery and mining from given data. It refers to general principles that should be used for choosing the data to be processed and for defining the way the data should be combined and connected.

Research limitations/implications

The research reported in this paper can be extended to define other new indicators regarding the quality of services offered to libraries by using a greater amount of data for analysis.

Practical implications

Changes should be made in the way of choosing data for analysis. The way of choosing data here is based on a methodology according to knowledge and data‐mining principles. A definition of new indicators about the quality of services in libraries should be derived from this methodology.

Originality/value

The new thinking in the paper is in the way librarians and decision‐makers in libraries have to use data. The paper shows a way of choosing data that will be able to produce useful conclusions after a well‐described analysis. The paper will be useful for librarians and library managers who want to plan strategies for improving the services they offer.

Details

Library Management, vol. 30 no. 3
Type: Research Article
ISSN: 0143-5124

Keywords

Article
Publication date: 27 July 2012

Freddie L. Barnard and Dale W. Nordquist

The purpose of this paper is to discuss the feasibility of preparing a statement of owner equity (SOE) and statement of cash flows (SOCF) for the agricultural sector…

664

Abstract

Purpose

The purpose of this paper is to discuss the feasibility of preparing a statement of owner equity (SOE) and statement of cash flows (SOCF) for the agricultural sector. Also, the use of the Agricultural Resource Management Survey (ARMS) to collect data needed to supplement the US farm sector accounts to prepare a sector SOE and SOCF is discussed.

Design/methodology/approach

An SOE and SOCF for an individual producer was used to provide an example format for preparing an SOE and SOCF for the agricultural sector and to identify the data needed from the ARMS survey to supplement farm sector accounts.

Findings

The format and data needed to prepare a sector SOE and SOCF were identified and the feasibility of the collection of that data using current ERS/USDA survey collection methods would provide the data needed to prepare the statements. However, the use of two independent data collection authorities to collect the data would result in an agricultural sector SOE and SOCF that would not reconcile.

Originality/value

The paper initiates a dialog of possible alternatives available to the ERS/USDA and researchers concerning data needed and data sources available to prepare an agricultural sector SOE and SOCF, as well as the shortfalls and inaccuracies that would result.

Article
Publication date: 21 June 2022

Muhammad Fauzan Ansyari, Wim Groot and Kristof De Witte

Professional development interventions (PDIs) are crucial for equipping teachers to use data effectively. Relying on previous studies reporting on such interventions, this…

Abstract

Purpose

Professional development interventions (PDIs) are crucial for equipping teachers to use data effectively. Relying on previous studies reporting on such interventions, this paper aims to identify and synthesise the goals, dimensions and conditions of PDIs for data use. This paper also examines the evidence of the effect of such interventions on student outcomes.

Design/methodology/approach

In this study, the authors employ a systematic literature review and meta-analysis to analyse teacher PDIs for data use.

Findings

The results suggest that conceptual, practical and continual goals are identified in data use PDIs. Supported by conceptual, practical or normative tools, facilitators employ a variety of techniques in facilitating teachers’ data use through data teams or professional learning communities. The facilitation techniques include assessing needs, using models or modelling, observing performance, providing feedback, providing built-in time for reflection and brokering. Further, the results highlight the influence of several conditions that contribute to the success of the interventions. Finally, the meta-analysis shows a significant positive effect of the interventions on student outcomes, with an effect size of 0.17.

Research limitations/implications

The authors' proposed framework should be empirically tested and validated through field studies in various contexts. Since the authors focussed on studies reporting data use PDIs for instructional purposes as well as providing the descriptions of the PDIs, the number of included studies was only 27 and represented only four countries. Of the 27, 10 studies were used for the meta-analysis and the results may be subject to publication bias. Seemingly, the result may be related to the authors' inclusion/exclusion criteria that only included peer-reviewed journal articles and excluded non-peer-reviewed studies such as theses or dissertations. This criterion potentially neglected some relevant studies.

Practical implications

Policymakers interested in developing a data use PDI should take into account the various goals of data use PDIs, depending on policymakers' interests. Building teachers’ understanding of data use can be addressed by the practical goals. This can be conducted within a short period of time through training or courses, either in-person or online. This is appropriate for an initiation strategy for data use within schools. However, targeting specific skills and dispositional attributes around data use should adopt practical and continual goals. These types of goals require a PDI with a sustained duration embedded in teachers’ classroom practices; therefore, political and practical support is necessary.

Social implications

The authors argue that the review findings contribute to knowledge and insights by presenting data use PDIs that support teacher learning, implementation and sustainability of data use practices.

Originality/value

This article provides a proposed framework for studying teacher PDIs for data use and sheds light on several goals, a variety of facilitation strategies and conditions and the effect of the interventions on student outcomes.

Details

Journal of Professional Capital and Community, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2056-9548

Keywords

Article
Publication date: 3 May 2022

Carlos Alberto Escobar, Daniela Macias, Megan McGovern, Marcela Hernandez-de-Menendez and Ruben Morales-Menendez

Manufacturing companies can competitively be recognized among the most advanced and influential companies in the world by successfully implementing Quality 4.0. However…

Abstract

Purpose

Manufacturing companies can competitively be recognized among the most advanced and influential companies in the world by successfully implementing Quality 4.0. However, its successful implementation poses one of the most relevant challenges to the Industry 4.0. According to recent surveys, 80%–87% of data science projects never make it to production. Regardless of the low deployment success rate, more than 75% of investors are maintaining or increasing their investments in artificial intelligence (AI). To help quality decision-makers improve the current situation, this paper aims to review Process Monitoring for Quality (PMQ), a Quality 4.0 initiative, along with its practical and managerial implications. Furthermore, a real case study is presented to demonstrate its application.

Design/methodology/approach

The proposed Quality 4.0 initiative improves conventional quality control methods by monitoring a process and detecting defective items in real time. Defect detection is formulated as a binary classification problem. Using the same path of Six Sigma define, measure, analyze, improve, control, Quality 4.0-based innovation is guided by Identify, Acsensorize, Discover, Learn, Predict, Redesign and Relearn (IADLPR2) – an ad hoc seven-step problem-solving approach.

Findings

The IADLPR2 approach has the ability to identify and solve engineering intractable problems using AI. This is especially intriguing because numerous quality-driven manufacturing decision-makers consistently cite difficulties in developing a business vision for this technology.

Practical implications

From the proposed method, quality-driven decision-makers will learn how to launch a Quality 4.0 initiative, while quality-driven engineers will learn how to systematically solve intractable problems through AI.

Originality/value

An anthology of the own projects enables the presentation of a comprehensive Quality 4.0 initiative and reports the approach’s first case study IADLPR2. Each of the steps is used to solve a real General Motors’ case study.

Details

International Journal of Lean Six Sigma, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-4166

Keywords

Open Access
Article
Publication date: 17 May 2022

M'hamed Bilal Abidine, Mourad Oussalah, Belkacem Fergani and Hakim Lounis

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to…

Abstract

Purpose

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

Design/methodology/approach

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Findings

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

Practical implications

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

Originality/value

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

Details

Sensor Review, vol. 42 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

Abstract

Details

Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

Book part
Publication date: 29 January 2013

Abby Sneade

Purpose — The Department for Transport's 2011 GPS National Travel Survey (NTS) pilot study investigated whether personal GPS devices and automated data processing could be…

Abstract

Purpose — The Department for Transport's 2011 GPS National Travel Survey (NTS) pilot study investigated whether personal GPS devices and automated data processing could be used in place of the 7-day paper diary. Using GPS technology could reduce the relatively high burden that the diary places upon respondents, reduce costs and improve data quality.

Design/methodology/approachData was collected from c.900 respondents. Practical changes were made to the existing methodology where necessary, including the collection of information to support data processing. Processing was undertaken using the University of Eindhoven's Trace Annotator. Results from the GPS pilot were then compared to those from the main NTS diaries for the same period.

Findings — There were no insurmountable problems using GPS devices to collect data; however, the processed GPS data did not resemble the diary outputs, making GPS unsuitable for the NTS. The GPS data produced fewer and longer trips than the diary data. The purpose of a quarter of the GPS trips was unclear, and a disproportionate share started and ended at home.

Research limitations — Further work to manually inspect trips identified via validation as unfeasible and subsequently refine the processing algorithms would have been desirable had time permitted. GPS data processing may have been hindered by missing GPS data, particularly in the case of rail travel.

Originality/value — This research used an accelerometer-equipped GPS device to better predict the method of travel. It also combined addresses that respondents reported having visited during the travel week with GIS data to code the purpose of trips without using a post-processing prompted-recall survey.

Details

Transport Survey Methods
Type: Book
ISBN: 978-1-78-190288-2

Keywords

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