An integrated approach to deploy data warehouse in business intelligence environment
16 Mar 2015-pp 1-4
TL;DR: An integrated architecture to manage and design business intelligence environment by co-ordinating several associated entities to achieve business agility is presented.
Abstract: Business Intelligence (BI) provides historical, current and predictive views of business operations with the help of some technologies, that include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. As analytics plays a major role in BI, OLAP is an integrated part of BI in modern day business application. Data warehouse is the most popular way to design and build OLAP. Data warehouse along with ETL and reporting tools provides an integrated environment for business processing. Business processing also demands decision making system and knowledge representation. Moreover the data sources are physically distributed in different locations. Hence modern day business environment is a complex architecture with a numbers of entities. In this paper authors present an integrated architecture to manage and design business intelligence environment by co-ordinating several associated entities to achieve business agility.
Citations
More filters
01 Aug 2018
TL;DR: The research community is encouraged to acknowledge the effectiveness of Business Intelligence (BI) applied to organizations at large as well as small scale.
Abstract: Since many decades, organizations have been using Business Intelligence (BI) as a foundation to organizational growth and credibility. This awareness has risen because of increased market competition and aspiration to make organizational processes more efficient. Data Warehouse (DW) emerged as a system used for reporting and data analysis. This paper scratches the surface of application of Data Warehouse (DW) in Business Intelligence (BI) and Decision Support Systems. The research community is encouraged to acknowledge the effectiveness of Business Intelligence (BI) applied to organizations at large as well as small scale.
11 citations
Cites background from "An integrated approach to deploy da..."
...Business processing demands not only decisionmaking systems but also knowledge representation that makes it easier for people with lower understanding of technology, such as managers and executives, to formulate schemes to help grow the business [5]....
[...]
...Sen [5] suggested that since the old data has same value as fresh data, Business Intelligence (BI) has to be applied in order to generate reports that provide insights to the past and present performance of the organization....
[...]
Dissertation•
01 Jan 2017
TL;DR: Findings indicate theoretical and practical contributions for developers to develop efficient BI applications using DV technique and show sufficient indications that by adopting DVDeM model in developing a system, the usability of the produced system is perceived by the majority of respondents as high and is able to support near real time decision making data.
Abstract: The main purpose of Business Intelligence (BI) is to focus on supporting an organization‘s strategic, operational and tactical decisions by providing comprehensive, accurate and vivid data to the decision makers. A data warehouse (DW), which is considered as the input for decision making system activities is created through a complex process known as Extract, Transform and Load (ETL). ETL operates at pre-defined times and requires time to process and transfer data.
However, providing near real time information to facilitate the data integration in supporting decision making process is a known issue. Inaccessibility to near realtime information could be overcome with Data Virtualization (DV) as it provides unified, abstracted, near real time, and encapsulated view of information for
querying. Nevertheless, currently, there are lack of studies on the BI model for developing and managing data in virtual manner that can fulfil the organization needs. Therefore, the main aim of this study is to propose a DV model for near-real time decision making in BI environment. Design science research methodology was adopted to accomplish the research objectives. As a result of this study, a model
called Data Virtualization Development Model (DVDeM) is proposed that addresses the phases and components which affect the BI environment. To validate the model, expert reviews and focus group discussions were conducted. A prototype based on the proposed model was also developed, and then implemented in two case studies.
Also, an instrument was developed to measure the usability of the prototype in providing near real time data. In total, 60 participants were involved and the findings indicated that 93% of the participants agreed that the DVDeM based prototype was able to provide near real-time data for supporting decision-making process. From the studies, the findings also showed that the majority of the participants (more than
90%) in both of education and business sectors, have affirmed the workability of the DVDeM and the usability of the prototype in particular able to deliver near real-time decision-making data. Findings also indicate theoretical and practical contributions for developers to develop efficient BI applications using DV technique. Also, the mean values for each measurement item are greater than 4 indicating that the respondents agreed with the statement for each measurement item. Meanwhile, it was
found that the mean scores for overall usability attributes of DVDeM design model fall under "High" or "Fairly High". Therefore, the results show sufficient indications that by adopting DVDeM model in developing a system, the usability of the produced system is perceived by the majority of respondents as high and is able to support near real time decision making data.
11 citations
Book•
01 Jan 2018
TL;DR: In this paper, aplicación de la especialización inteligente, gracias al trabajo colaborativo, se combina al sector agropecuario con las tecnologias, matematicas, estadistica, and las ciencias computacionales, for optimizacion de los procesos productivos.
Abstract: El analisis de datos es un proceso complejo que trata de encontrar patrones utiles y relaciones entre los datos a fin de obtener informacion sobre un problema especifico y de esta manera tomar decisiones acertadas para su solucion. Las tecnicas de analisis de datos que son exploradas en el presente libro son actualmente utilizadas en diversos sectores de la economia. En un inicio, fueron empleadas por las grandes empresas a fin de incrementar sus rendimientos financieros. El libro se basa en la aplicacion de la especializacion inteligente, de este modo, gracias al trabajo colaborativo, se combina al sector agropecuario con las tecnologias, matematicas, estadistica y las ciencias computacionales, para la optimizacion de los procesos productivos.
6 citations
01 Sep 2018
TL;DR: The Integrated Proposed Architecture (IPA) is deployed on W category hospital in order to manage and monitor the data effectively for analysis and decision making.
Abstract: Business Intelligence (BI) is the process to extract information from data then get knowledge from that information to take the decisions. This paper shows the effectiveness of BI technologies with data warehouse, for decision making. In this paper, we deploy the Integrated Proposed Architecture (IPA) on W category hospital in order to manage and monitor the data effectively for analysis and decision making. The accuracy of IPA is 93% in term of information analysis that is 6% better than Traditional Data warehouse Architecture (TDA). The IPA is also able to support dashboard management, multidimensional data model, perform online analytical processing, perform user authentication and generate dynamic reports via BI technologies.
3 citations
Cites background or methods from "An integrated approach to deploy da..."
...In [3], Author proposed integrated approach to deploy DW in BI environment to perform analytical processing....
[...]
...Generate efficient reports and performed query processing which incorporates Data Marts (DM), DW and Virtual Data Warehouse (VDW) [3]....
[...]
...[3] Integrated DWA, not perform real-time analysis, data security issues...
[...]
...In [3], Author perform Online Analytical Processing (OLAP) and dashboard management was done in [5]....
[...]
01 Apr 2019
TL;DR: In this article, a metodología for the diseño of a sistema de soporte decisión that será aplicado in un contexto educativo is presented.
Abstract: El desarrollo de las tecnologías de información y comunicación han facilitado el uso de herramientas tecnológicas en numerosos ámbitos. Esto ha propiciado la generación de información en volúmenes significativos, que dificulta la posibilidad de extraer decisiones basadas en situaciones detectadas como no deseables. Es por eso que las organizaciones necesitan la integración de herramientas tecnológicas adecuadas para poder adaptarse a las exigencias del medio y detectar de manera más ágil las posibilidades de mejora. Por lo tanto este proyecto pretende el desarrollo de una metodología para el diseño de un sistema de soporte decisión que será aplicado en un contexto educativo. . Palabras claves: inteligencia de negocios, paradigma analítico, sistema de soporte de decisión, educación.
2 citations
Cites background from "An integrated approach to deploy da..."
...Este tipo de herramientas se clasifican en dos: los sistemas de soporte de decisiones y los sistemas de información para ejecutivos [9][10][11][12]....
[...]
References
More filters
TL;DR: A technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance and lets users analyze business performance in highly distributed environments with a short time response is presented.
Abstract: Continuous improvement of business processes is a challenging task that requires complex and robust supporting systems. Using advanced analytics methods and emerging technologies--such as business intelligence systems, business activity monitoring, predictive analytics, behavioral pattern recognition, and "type simulations"--can help business users continuously improve their processes. However, the high volumes of event data produced by the execution of processes during the business lifetime prevent business users from efficiently accessing timely analytics data. This article presents a technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance. The proposed architecture lets users analyze business performance in highly distributed environments with a short time response. This article is part of a special issue on leveraging big data and business analytics.
97 citations
"An integrated approach to deploy da..." refers background in this paper
...Business Analytical Service Unit (BASU) [3] is proposed to access very large amount of data to analyze business processing incorporating apache hadoop, HBase and Hive....
[...]
11 Mar 2011
TL;DR: This paper mainly focus on data preprocessing stage of the first phase of web usage mining with activities like field extraction and data cleaning algorithms, which eliminates inconsistent or unnecessary items in the analyzed data.
Abstract: Web usage mining (WUM) is a type of web mining, which exploits data mining techniques to extract valuable information from navigation behavior of World Wide Web users. The data should be preprocessed to improve the efficiency and ease of the mining process. So it is important to define before applying data mining techniques to discover user access patterns from web log. The main task of data preprocessing is to prune noisy and irrelevant data, and to reduce data volume for the pattern discovery phase. This paper mainly focus on data preprocessing stage of the first phase of web usage mining with activities like field extraction and data cleaning algorithms. Field extraction algorithm performs the process of separating fields from the single line of the log file. Data cleaning algorithm eliminates inconsistent or unnecessary items in the analyzed data.
79 citations
01 Mar 2010
TL;DR: This paper describes the QoX optimizer that considers multiple design strategies and finds an ETL design that satisfies multiple objectives, and defines the optimizer search space, cost functions, and search algorithms.
Abstract: Extract-Transform-Load (ETL) processes play an important role in data warehousing. Typically, design work on ETL has focused on performance as the sole metric to make sure that the ETL process finishes within an allocated time window. However, other quality metrics are also important and need to be considered during ETL design. In this paper, we address ETL design for performance plus fault-tolerance and freshness. There are many reasons why an ETL process can fail and a good design needs to guarantee that it can be recovered within the ETL time window. How to make ETL robust to failures is not trivial. There are different strategies that can be used and they each have different costs and benefits. In addition, other metrics can affect the choice of a strategy; e.g., higher freshness reduces the time window for recovery. The design space is too large for informal, ad-hoc approaches. In this paper, we describe our QoX optimizer that considers multiple design strategies and finds an ETL design that satisfies multiple objectives. In particular, we define the optimizer search space, cost functions, and search algorithms. Also, we illustrate its use through several experiments and we show that it produces designs that are very near optimal.
76 citations
"An integrated approach to deploy da..." refers methods in this paper
...QoX optimizer [15] considers multiple design strategies and finds an ETL design that satisfies multiple desirable objectives such as the optimizing search space, cost functions and search algorithms....
[...]
TL;DR: This paper proposes a partition algorithm that divides all data sites into incomparable groups such that the skyline computations in all groups can be parallelized without changing the final result, and develops a novel algorithm framework called PaDSkyline for parallel skyline query processing among partitioned site groups.
Abstract: The skyline of a multidimensional point set is a subset of interesting points that are not dominated by others. In this paper, we investigate constrained skyline queries in a large-scale unstructured distributed environment, where relevant data are distributed among geographically scattered sites. We first propose a partition algorithm that divides all data sites into incomparable groups such that the skyline computations in all groups can be parallelized without changing the final result. We then develop a novel algorithm framework called PaDSkyline for parallel skyline query processing among partitioned site groups. We also employ intragroup optimization and multifiltering technique to improve the skyline query processes within each group. In particular, multiple (local) skyline points are sent together with the query as filtering points, which help identify unqualified local skyline points early on a data site. In this way, the amount of data to be transmitted via network connections is reduced, and thus, the overall query response time is shortened further. Cost models and heuristics are proposed to guide the selection of a given number of filtering points from a superset. A cost-efficient model is developed to determine how many filtering points to use for a particular data site. The results of an extensive experimental study demonstrate that our proposals are effective and efficient.
51 citations
"An integrated approach to deploy da..." refers background or methods in this paper
...The problem of constrained skyline query processing against distributed data sites is discussed in [26]....
[...]
...It developed a new algorithm framework called PaDSkyline [26] for parallel skyline query processing among the partitioned sites....
[...]
11 Apr 2011
TL;DR: This paper introduces NEEL, a CEP query language for expressing nested CEP pattern queries composed of sequence, negation, AND and OR operators, and introduces several strategies for efficient shared processing of groups of normalized NEEL subexpressions to conserve CPU and memory consumption.
Abstract: Complex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to business intelligence. These monitoring applications submit complex queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of flat sequence queries only. To assure real-time responsiveness and scalability for pattern detection even on huge volume high-speed streams, efficient processing techniques must be designed. In this paper, we first analyze the prevailing nested pattern query processing strategy and identify several serious shortcomings. Not only are substantial subsequences first constructed just to be subsequently discarded, but also opportunities for shared execution of nested subexpressions are overlooked. As foundation, we introduce NEEL, a CEP query language for expressing nested CEP pattern queries composed of sequence, negation, AND and OR operators. To overcome deficiencies, we design rewriting rules for pushing negation into inner subexpressions. Next, we devise a normalization procedure that employs these rules for flattening a nested complex event expression. To conserve CPU and memory consumption, we propose several strategies for efficient shared processing of groups of normalized NEEL subexpressions. These strategies include prefix caching, suffix clustering and customized “bit-marking” execution strategies. We design an optimizer to partition the set of all CEP subexpressions in a NEEL normal form into groups, each of which can then be mapped to one of our shared execution operators. Lastly, we evaluate our technologies by conducting a performance study to assess the CPU processing time using real-world stock trades data. Our results confirm that our NEEL execution in many cases performs 100 fold faster than the traditional iterative nested execution strategy for real stock market query workloads.
50 citations
"An integrated approach to deploy da..." refers methods in this paper
...The query analyzing is focused to support nested query specification and execution in the Complex event processing (CEP) context [27]....
[...]