scholarly journals Succeskriterier for anvendelse af Business Intelligence i universitetssektoren

2019 ◽  
Vol 14 (27) ◽  
pp. 50-65
Author(s):  
Rikke Gaardboe ◽  
Tom Nyvang ◽  
Erling Jensen
Keyword(s):  
Business Intelligence ◽  
Learning Analytics ◽  
Ad Hoc

Gennem de sidste 10-15 år har universitetssektoren i Danmark undergået en række reformer og forandringer. En konsekvens heraf er et behov for højere grad af styring, som blandt andet understøttes af Business Intelligence (BI). BI er et begreb, der beskriver teknologier, software og processer til at tilvejebringe og analysere data til brug for beslutningstagning. I denne artikel undersøges, hvordan universiteter kan opnå succes med BI, samt hvilken organisatorisk nytte teknologien har. Studiet viser, at jo højere systemkvalitet, des højere brugertilfredshed og mere brug af BI. Øget informationskvalitet påvirker brugertilfredsheden. Den individuelle nytte af BI påvirkes af brugertilfredshed og brug. Der opnås organisatorisk nytte ved at anvende BI til rapportering, til ad hoc-analyser samt til opfølgning på forløb. Til trods for at teknologien kan anvendes til learning analytics, er det ikke med det formål, teknologien er implementeret, men mere i relation til nøgletal for økonomisk rapportering og kvalitetsmålinger.

scholarly journals Unleashing The Power of Your Master Linkage Map – Is There A Role For Business Intelligence Tools In Supporting Data Linkage?

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Brian Stokes
Keyword(s):  
Business Intelligence ◽  
Ad Hoc ◽  
Data Linkage ◽  
Query Language ◽  
Ease Of Use ◽  
Size Estimation ◽  
Cohort Size ◽  
Data Services ◽  
Technical Staff ◽  
Software Applications

Background with rationaleBusiness Intelligence (BI) software applications collect and process large amounts of data from one or more sources, and for a variety of purposes. These can include generating operational or sales reports, developing dashboards and data visualisations, and for ad-hoc analysis and querying of enterprise databases. Main AimBusiness Intelligence (BI) software applications collect and process large amounts of data from one or more sources, and for a variety of purposes. These can include generating operational or sales reports, developing dashboards and data visualisations, and for ad-hoc analysis and querying of enterprise databases. Methods/ApproachIn deciding to develop a series of dashboards to visually represent data stored in its MLM, the TDLU identified routine requests for these data and critically examined existing techniques for extracting data from its MLM. Traditionally Structured Query Language (SQL) queries were developed and used for a single purpose. By critically analysing limitations with this approach, the TDLU identified the power of BI tools and ease of use for both technical and non-technical staff. ResultsImplementing a BI tool is enabling quick and accurate production of a comprehensive array of information. Such information assists with cohort size estimation, producing data for routine and ad-hoc reporting, identifying data quality issues, and to answer questions from prospective users of linked data services including instantly producing estimates of links stored across disparate datasets. Conclusion BI tools are not traditionally considered integral to the operations of data linkage units. However, the TDLU has successfully applied the use of a BI tool to enable a rich set of data locked in its MLM to be quickly made available in multiple, easy to use formats and by technical and non-technical staff.

An architecture for ad-hoc and collaborative business intelligence

2010 ◽  
Author(s):  
Henrike Berthold ◽  
Philipp Rösch ◽  
Stefan Zöller ◽  
Felix Wortmann ◽  
Alessio Carenini ◽  
...  
Keyword(s):  
Business Intelligence ◽  
Ad Hoc

Towards Automation of Business Intelligence Services Using Hybrid Intelligent System Approach

2013 ◽  
Vol 4 (4) ◽  
pp. 61-92 ◽  
Author(s):  
Rajendra M. Sonar
Keyword(s):  
Business Intelligence ◽  
Ad Hoc ◽  
Intelligent System ◽  
Value Added ◽  
Case Based Reasoning ◽  
Intelligent Technique ◽  
Intelligence Services ◽  
Wide Range ◽  
Operational Systems ◽  
Human Decision

Business Intelligence (BI) includes many tools, techniques and technologies. BI processes often involve team of human decision makers and end-users to extract, explore and analyse the data. The results, decisions or models after analysis need to be implemented into operational systems. There can be considerable time delay between business events happening and action taken thus loosing opportunities. Intelligent techniques such as rule-based reasoning and case-based reasoning have been used extensively to address wide range of intelligent tasks including personalisation and recommendation. Some BI tasks can be modelled, automated and delivered through services rather than done on ad-hoc basis. The authors represent a service based approach to BI where a service corresponds to a well defined analytical functionality implemented using intelligent technique(s), filtering techniques or hybrids of them accessing only relevant data from database specifically modelled and designed for such tasks. The authors discuss an application of the approach for a value-added service in mobile domain.

scholarly journals Investigation on Blockchain Technology for Web Service Composition

2021 ◽  
Vol 18 (1) ◽  
pp. 1-23
Author(s):  
Sridevi S. ◽  
Karpagam G. R. ◽  
Vinoth Kumar B. ◽  
Uma Maheswari J.
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Business Intelligence ◽  
Service Composition ◽  
Banking Sector ◽  
Ad Hoc ◽  
Web Service Composition ◽  
Semantic Web Service ◽  
Blockchain Technology ◽  
Semantic Web Service Composition

The blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value. Blockchain technology makes breakthroughs in business intelligence in many areas such as banking sector, finance, judiciary, commerce, and information technology. Web service compositions have a revolutionary impact on business intelligence by enabling loose coupling, data consolidation from diverse sources, consolidation of information under a single roof, easing ad-hoc querying and reporting. The objective of current work is to investigate the applicability of blockchain for the semantic web service composition process. The paper focuses on design of conceptual architecture and the algorithm for QoS-aware semantic web service composition (SWSC) using blockchain.

scholarly journals Business Intelligence From E -Connotations to Technological Trends

2013 ◽  
Vol 60 (2) ◽  
pp. 249-257
Author(s):  
Daniel Homocianu ◽  
Dinu Airinei
Keyword(s):  
Real Time ◽  
Business Intelligence ◽  
Ad Hoc ◽  
Time Response ◽  
Organizational Changes

Abstract This article shows most of the E -connotations when approaching the Business Intelligence (BI) field not just as methodology but also as practical implementations meant to serve as a support for organizational decisions. In the last part of the paper the focus is moved to nowadays technological possibilities and trends related to Business Intelligence. Some aspects as the specificity of BI applications, their promises, the problem of real-time response and some other limitations and resolved issues related to their capability to respond to ad-hoc organizational changes by changing their behaviour and feed-back are taken into consideration.

scholarly journals Learning Analytics focused on student behavior. Case study: dropout in distance learning institutions

2017 ◽  
Author(s):  
Jose Aguilar ◽  
Guido Riofrío ◽  
Eduardo Encalada
Keyword(s):  
Distance Learning ◽  
Learning Process ◽  
Business Intelligence ◽  
Learning Analytics ◽  
Student Behavior ◽  
University Studies ◽  
Educación A Distancia ◽  
Learning Institutions ◽  
Learning Analysis

Abstract: Normally, Learning Analytics (LA) can be focused on the analysis of the learning process or the student behavior. In this paper is analyzed the use of LA in the context of distance learning universities, particularly focuses on the students’ behavior. We propose to use a new concept, called "Autonomic Cycle of Learning Analysis Tasks", which defines a set of tasks of LA, whose common objective is to achieve an improvement in the process under study. In this paper, we develop the "Autonomic Cycle of LA Tasks" to analyze the dropout in distance learning institutions. We use a business intelligence methodology in order to develop the "Autonomic Cycle of LA Tasks" for the analysis of the dropout in distance learning. The Autonomic Cycle identifies factors that influence the decision of a student to abandon their studies, predicts the potentially susceptible students to abandon their university studies, and define a motivational pattern for these students.  Spanish Abstract: Normalmente, La Análitica del Aprendizaje puede enfocarse en el análisis del proceso de aprendizaje, o en el análisis del comportamiento del estudiante. En este artículo se analiza el uso de LA en el contexto de las universidades a distancia, centrándonos particularmente en el comportamiento de los estudiantes. Para ello, proponemos utilizar un nuevo concepto, llamado "Ciclo Autonómico de Tareas de Análitica del Aprendizaje", que define un conjunto de tareas de LA, cuyo objetivo común es lograr una mejora en el proceso bajo estudio. En este artículo se desarrolla el "Ciclo Autonómico de Tareas LA" para analizar la deserción estudiantil en las instituciones de educación a distancia. Para ello, utilizamos una metodología de inteligencia de negocios con el fin de desarrollar dicho ciclo para el análisis de la deserción en el aprendizaje a distancia. El Ciclo Autonómico identifica factores que influyen en la decisión del estudiante de abandonar sus estudios universitarios, predice los estudiantes potencialmente susceptibles a desertar, y define un patrón de motivación para estos estudiantes.

scholarly journals A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2923
Author(s):  
Alberto Huertas Celdrán ◽  
José A. Ruipérez-Valiente ◽  
Félix J. García Clemente ◽  
María Jesús Rodríguez-Triana ◽  
Shashi Kant Shankar ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Ad Hoc ◽  
Heterogeneous Data ◽  
Network Function Virtualization ◽  
Learning Tools ◽  
Multimodal Learning ◽  
Network Function ◽  
The Future ◽  
Multimodal Learning Analytics ◽  
Smart Classrooms

The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs. However, a review of different MMLA studies highlighted that ad-hoc and rigid architectures cannot be scaled up to real contexts. In this work, we propose a novel MMLA architecture that builds on software-defined networks and network function virtualization principles. We exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way. Additionally, through some experiments, we demonstrate the feasibility and performance of our architecture when different classroom devices are reconfigured with diverse learning tools. These findings and the proposed architecture can be useful for other researchers in the area of MMLA and educational technologies envisioning the future of smart classrooms. Future work should aim to deploy this architecture in real educational scenarios with MMLA applications.

scholarly journals Big data and learning analytics: Singular or plural?

First Monday ◽  
2017 ◽  
Vol 22 (4) ◽  
Author(s):  
Anna Wilson ◽  
Terrie Lynn Thompson ◽  
Cate Watson ◽  
Valerie Drew ◽  
Sarah Doyle
Keyword(s):  
Public Health ◽  
Big Data ◽  
Business Intelligence ◽  
Learning Analytics ◽  
Context Dependence ◽  
Physical Sciences ◽  
Explicit Consideration ◽  
Analytical Approaches ◽  
Data Context

Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation — learning analytics — with three other big data contexts — the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.

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