A framework to deploy Mobile Business Intelligence within Small and Medium Enterprises in developing countries

Abstract: Over the years, there has been emergence of varieties of decision-support applications. System evolved due to the rapid growth in data complexity and need for accurate information in a dynamic environment. Due to increase in mobility and automation of activities within enterprises, huge amounts of data are rapidly generated than they could be instantaneously utilized in heterogeneous, intra or interorganisational business processes. Developing countries’ Small and Medium Enterprises (SMEs) are faced with challenges of accessing intelligent information for decision making at different operational sites. SMEs in developing countries are paying a huge business opportunity cost by not utilizing Mobile Business Intelligence (MBI) systems. This is as a result of lack of MBI framework to inform the deployment of MBI solutions in developing countries’ SMEs. This study therefore is envisioned to design a framework for the deployment of MBI in developing countries’ SMEs. So as to achieve this, the study adopted various scientific approaches (Textual Analysis, Principal Component Analysis, and Structural Equation Modelling) systematically. This study is expected to contribute towards the literature and the methods in establishing and determining the factors needed for the development of frameworks in Information Systems studies. Practically, the study is expected aid deployment of MBI for SMEs in developing countries’ contexts

State-of-the-art review and critical success factors for mobile business intelligence

Due to ubiquitous information requirements, market interest in mobile business intelligence (BI) has grown markedly. However, mobile BI market is a relatively new area that has been driven primarily by the IT industry. Yet, there is a lack of systematic study on the critical success factors for mobile BI. This research reviews the state-of-the-art of mobile BI, and explores the critical success factors based on a rigorous examination of the academic and practitioner literature. The study reveals that critical success factors of mobile BI generally fall into four key dimensions, namely security, mobile technology, system content and quality, and organisational support perspectives. The various research findings will be useful to organisations which are considering or undertaking mobile business intelligence initiatives

Continuous Workflows: From Model to Enactment System

Workflows are actively being used in both business and scientific domains to automate processes and facilitate collaboration. A workflow management (or enactment) system (WfMS) defines, creates and manages the execution of workflows on one or more workflow engines, which are able to interpret workflow definitions, allocate resources, interact with workflow participants and, where required, invoke the needed tools (e.g., databases, job schedulers, etc.) and applications. Traditional WfMSs and workflow design processes view the workflow as a one-time interaction with the various data sources, i.e., when a workflow is invoked, its steps are executed once and in-order. The fundamental underlying assumption has been that data sources are passive and all interactions are structured along the request/reply (query) model. Hence, traditional WfMS cannot effectively support business or scientific monitoring applications that require the processing of data streams such as those generated by sensing devices as well as mobile and web applications. It is the hypothesis of this dissertation that Workflow Management Systems can be extended to support data stream semantics to enable monitoring applications. This includes the ability to apply flexible bounds on unbounded data streams and the ability to facilitate on-the-fly processing of bounded bundles of data (window semantics). To support this hypothesis this dissertation has produced new specifications, a design, an implementation and a thorough evaluation of a novel Continuous Workflows (CWf) model, which is backwards compatible with currently available workflow models. The CWf model was implemented in a CONtinuous workFLow ExeCution Engine, CONFLuEnCE, as an extension of Kepler, which is a popular scientific WfMS. The applicability of the CWf model in both scientific and business applications was demonstrated by utilizing CONFLuEnCE in Astroshelf to support live annotations (i.e., monitoring of astronomical data), and to support supply chain monitoring and management. The implementation of CONFLuEnCE led to the realization that different applications have different performance requirements and hence an integrated workflow scheduling framework is essential. Towards meeting this need, STAFiLOS, a Stream FLOw Scheduling framework for Continuous Workflows, was designed and implemented, within CONFLuEnCE. The performance of STAFiLOS was evaluated using the Linear Road Benchmark for continuous workflows

Mobile Business Intelligence & Business Intelligence - Kritiska framgångsfaktorer för mobile business intelligence & business intelligence

The connection between supply chains, markets and businesses is getting more and more complex and it can be a big problem for enterprises to solve. One solution to the problem is the usage of a business intelligence system. This system helps the organization analyze critical data that can lead to saving resources by making the right decisions. The world is getting more and more mobile, and the BI-system has to adjust to this, hence the mobile business intelligence. This solution provides the same information as the BI system does, straight to the users smartphone or touchpad, and can be used wherever they may be. We want to determine what the critical success factors for such a system may be. By researching the theoretical papers available we came up with a list of 13 factors that are claimed to be critical for the success of a mobile BI system. We then interviewed five people with experience in this area to determine which factors are the most critical. This will then be presented as a complete, ranked list of the most critical success factors you need to emphasize when implementing a new mobile BI system. Our analysis of our empirics showed that the most critical factors were the ones involving the management of the organization. The least critical factors were the ones regarding technical aspects of the device that the BI is on

Semantic metadata for supporting exploratory OLAP

Cotutela Universitat Politècnica de Catalunya i Aalborg UniversitetOn-Line Analytical Processing (OLAP) is an approach widely used for data analysis. OLAP is based on the multidimensional (MD) data model where factual data are related to their analytical perspectives called dimensions and together they form an n-dimensional data space referred to as data cube. MD data are typically stored in a data warehouse, which integrates data from in-house data sources, and then analyzed by means of OLAP operations, e.g., sales data can be (dis)aggregated along the location dimension. As OLAP proved to be quite intuitive, it became broadly accepted by non-technical and business users. However, as users still encountered difficulties in their analysis, different approaches focused on providing user assistance. These approaches collect situational metadata about users and their actions and provide suggestions and recommendations that can help users' analysis. However, although extensively exploited and evidently needed, little attention is paid to metadata in this context. Furthermore, new emerging tendencies call for expanding the use of OLAP to consider external data sources and heterogeneous settings. This leads to the Exploratory OLAP approach that especially argues for the use of Semantic Web (SW) technologies to facilitate the description and integration of external sources. With data becoming publicly available on the (Semantic) Web, the number and diversity of non-technical users are also significantly increasing. Thus, the metadata to support their analysis become even more relevant. This PhD thesis focuses on metadata for supporting Exploratory OLAP. The study explores the kinds of metadata artifacts used for the user assistance purposes and how they are exploited to provide assistance. Based on these findings, the study then aims at providing theoretical and practical means such as models, algorithms, and tools to address the gaps and challenges identified. First, based on a survey of existing user assistance approaches related to OLAP, the thesis proposes the analytical metadata (AM) framework. The framework includes the definition of the assistance process, the AM artifacts that are classified in a taxonomy, and the artifacts organization and related types of processing to support the user assistance. Second, the thesis proposes a semantic metamodel for AM. Hence, Resource Description Framework (RDF) is used to represent the AM artifacts in a flexible and re-usable manner, while the metamodeling abstraction level is used to overcome the heterogeneity of (meta)data models in the Exploratory OLAP context. Third, focusing on the schema as a fundamental metadata artifact for enabling OLAP, the thesis addresses some important challenges on constructing an MD schema on the SW using RDF. It provides the algorithms, method, and tool to construct an MD schema over statistical linked open data sets. Especially, the focus is on enabling that even non-technical users can perform this task. Lastly, the thesis deals with queries as the second most relevant artifact for user assistance. In the spirit of Exploratory OLAP, the thesis proposes an RDF-based model for OLAP queries created by instantiating the previously proposed metamodel. This model supports the sharing and reuse of queries across the SW and facilitates the metadata preparation for the assistance exploitation purposes. Finally, the results of this thesis provide metadata foundations for supporting Exploratory OLAP and advocate for greater attention to the modeling and use of semantics related to metadata.El processament analític en línia (OLAP) és una tècnica àmpliament utilitzada per a l'anàlisi de dades. OLAP es basa en el model multi-dimensional (MD) de dades, on dades factuals es relacionen amb les seves perspectives analítiques, anomenades dimensions, i conjuntament formen un espai de dades n-dimensional anomenat cub de dades. Les dades MD s'emmagatzemen típicament en un data warehouse (magatzem de dades), el qual integra dades de fonts internes, les quals posteriorment s'analitzen mitjançant operacions OLAP, per exemple, dades de vendes poden ser (des)agregades a partir de la dimensió ubicació. Un cop OLAP va ser provat com a intuïtiu, va ser ampliament acceptat tant per usuaris no tècnics com de negoci. Tanmateix, donat que els usuaris encara trobaven dificultats per a realitzar el seu anàlisi, diferents tècniques s'han enfocat en la seva assistència. Aquestes tècniques recullen metadades situacionals sobre els usuaris i les seves accions, i proporcionen suggerències i recomanacions per tal d'ajudar en aquest anàlisi. Tot i ésser extensivament emprades i necessàries, poca atenció s'ha prestat a les metadades en aquest context. A més a més, les noves tendències demanden l'expansió d'ús d'OLAP per tal de considerar fonts de dades externes en escenaris heterogenis. Això ens porta a la tècnica d'OLAP exploratori, la qual es basa en l'ús de tecnologies en la web semàntica (SW) per tal de facilitar la descripció i integració d'aquestes fonts externes. Amb les dades essent públicament disponibles a la web (semàntica), el nombre i diversitat d'usuaris no tècnics també incrementa signifícativament. Així doncs, les metadades per suportar el seu anàlisi esdevenen més rellevants. Aquesta tesi doctoral s'enfoca en l'ús de metadades per suportar OLAP exploratori. L'estudi explora els tipus d'artefactes de metadades utilitzats per l'assistència a l'usuari, i com aquests són explotats per proporcionar assistència. Basat en aquestes troballes, l'estudi preté proporcionar mitjans teòrics i pràctics, com models, algorismes i eines, per abordar els reptes identificats. Primerament, basant-se en un estudi de tècniques per assistència a l'usuari en OLAP, la tesi proposa el marc de treball de metadades analítiques (AM). Aquest marc inclou la definició del procés d'assistència, on els artefactes d'AM són classificats en una taxonomia, i l'organització dels artefactes i tipus relacionats de processament pel suport d'assistència a l'usuari. En segon lloc, la tesi proposa un meta-model semàntic per AM. Així doncs, s'utilitza el Resource Description Framework (RDF) per representar els artefactes d'AM d'una forma flexible i reusable, mentre que el nivell d'abstracció de metamodel s'utilitza per superar l'heterogeneïtat dels models de (meta)dades en un context d'OLAP exploratori. En tercer lloc, centrant-se en l'esquema com a artefacte fonamental de metadades per a OLAP, la tesi adreça reptes importants en la construcció d'un esquema MD en la SW usant RDF. Proporciona els algorismes, mètodes i eines per construir un esquema MD sobre conjunts de dades estadístics oberts i relacionats. Especialment, el focus rau en permetre que usuaris no tècnics puguin realitzar aquesta tasca. Finalment, la tesi tracta amb consultes com el segon artefacte més rellevant per l'assistència a usuari. En l'esperit d'OLAP exploratori, la tesi proposa un model basat en RDF per consultes OLAP instanciant el meta-model prèviament proposat. Aquest model suporta el compartiment i reutilització de consultes sobre la SW i facilita la preparació de metadades per l'explotació de l'assistència. Finalment, els resultats d'aquesta tesi proporcionen els fonaments en metadades per suportar l'OLAP exploratori i propugnen la major atenció al model i ús de semàntica relacionada a metadades.On-Line Analytical Processing (OLAP) er en bredt anvendt tilgang til dataanalyse. OLAP er baseret på den multidimensionelle (MD) datamodel, hvor faktuelle data relateres til analytiske synsvinkler, såkaldte dimensioner. Tilsammen danner de et n-dimensionelt rum af data kaldet en data cube. Multidimensionelle data er typisk lagret i et data warehouse, der integrerer data fra forskellige interne datakilder, og kan analyseres ved hjælp af OLAPoperationer. For eksempel kan salgsdata disaggregeres langs sted-dimensionen. OLAP har vist sig at være intuitiv at forstå og er blevet taget i brug af ikketekniske og orretningsorienterede brugere. Nye tilgange er siden blevet udviklet i forsøget på at afhjælpe de problemer, som denne slags brugere dog stadig står over for. Disse tilgange indsamler metadata om brugerne og deres handlinger og kommer efterfølgende med forslag og anbefalinger, der kan bidrage til brugernes analyse. På trods af at der er en klar nytteværdi i metadata (givet deres udbredelse), har stadig ikke været meget opmærksomhed på metadata i denne kotekst. Desuden lægger nye fremspirende teknikker nu op til en udvidelse af brugen af OLAP til også at bruge eksterne og uensartede datakilder. Dette har ført til Exploratory OLAP, en tilgang til OLAP, der benytter teknologier fra Semantic Web til at understøtte beskrivelse og integration af eksterne kilder. Efterhånden som mere data gøres offentligt tilgængeligt via Semantic Web, kommer flere og mere forskelligartede ikketekniske brugere også til. Derfor er metadata til understøttelsen af deres dataanalyser endnu mere relevant. Denne ph.d.-afhandling omhandler metadata, der understøtter Exploratory OLAP. Der foretages en undersøgelse af de former for metadata, der benyttes til at hjælpe brugere, og af, hvordan sådanne metadata kan udnyttes. Med grundlag i disse fund søges der løsninger til de identificerede problemer igennem teoretiske såvel som praktiske midler. Det vil sige modeller, algoritmer og værktøjer. På baggrund af en afdækning af eksisterende tilgange til brugerassistance i forbindelse med OLAP præsenteres først rammeværket Analytical Metadata (AM). Det inkluderer definition af assistanceprocessen, en taksonomi over tilhørende artefakter og endelig relaterede processeringsformer til brugerunderstøttelsen. Dernæst præsenteres en semantisk metamodel for AM. Der benyttes Resource Description Framework (RDF) til at repræsentere AM-artefakterne på en genbrugelig og fleksibel facon, mens metamodellens abstraktionsniveau har til formål at nedbringe uensartetheden af (meta)data i Exploratory OLAPs kontekst. Så fokuseres der på skemaet som en fundamental metadata-artefakt i OLAP, og afhandlingen tager fat i vigtige udfordringer i forbindelse med konstruktionen af multidimensionelle skemaer i Semantic Web ved brug af RDF. Der præsenteres algoritmer, metoder og redskaber til at konstruere disse skemaer sammenkoblede åbne statistiske datasæt. Der lægges særlig vægt på, at denne proces skal kunne udføres af ikke-tekniske brugere. Til slut tager afhandlingen fat i forespørgsler som anden vigtig artefakt inden for bruger-assistance. I samme ånd som Exploratory OLAP foreslås en RDF-baseret model for OLAP-forespørgsler, hvor førnævnte metamodel benyttes. Modellen understøtter deling og genbrug af forespørgsler over Semantic Web og fordrer klargørelsen af metadata med øje for assistance-relaterede formål. Endelig leder resultaterne af afhandlingen til fundamenterne for metadata i støttet Exploratory OLAP og opfordrer til en øget opmærksomhed på modelleringen og brugen af semantik i forhold til metadataPostprint (published version