Integrated model-driven dashboard development

Palpanas, Themis; Chowdhary, Pawan; Mihaila, George A.; Pinel, Florian

doi:10.1007/s10796-007-9032-9

Cited by 44 publications

(46 citation statements)

References 14 publications

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“…The dashboard is a useful Business Intelligence tool, able to represent data in a very synthetic way, that contains all the elements really necessary for the business performance reporting process (Palpanas, Chowdhary, Mihaila, & Pinel, 2007). In the user interface, the indicators are visualized with a graphical support, represented by gauges, lines, and histograms charts.…”

Section: Dashboardmentioning

confidence: 99%

System Architecture for Approximate Query Processing

Tria¹,

Lefons²,

Tangorra³

2016

CIS

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Decision making is an activity that addresses the problem of extracting knowledge and information from data stored in data warehouses, in order to improve the business processes of information systems. Usually, decision making is based on On-Line Analytical Processing, data mining, or approximate query processing. In the last case, answers to analytical queries are provided in a fast manner, although affected with a small percentage of error. In the paper, we present the architecture of an approximate query answering system. Then, we illustrate our ADAP (Analytical Data Profile) system, which is based on an engine able to provide fast responses to the main statistical functions by using orthogonal polynomials series to approximate the data distribution of multidimensional relations. Moreover, several experimental results to measure the approximation error are shown and the response-time to analytical queries is reported.

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Section: Dashboardmentioning

confidence: 99%

System Architecture for Approximate Query Processing

Tria¹,

Lefons²,

Tangorra³

2016

CIS

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“…The architecture also includes a Dashboard for the visualization of results (which can also be generated from the Strategic Model [20]). We elaborate on these components in the following paragraphs.…”

Section: Monitoring the Strategic Objectives Of An Organization Is VImentioning

confidence: 99%

Strategic Management for Real-Time Business Intelligence

Zoumpatianos

Palpanas

Mylopoulos

2013

Lecture Notes in Business Information Processing

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Abstract. Even though much research has been devoted on real-time data warehousing, most of it ignores business concerns that underlie all uses of such data. The complete Business Intelligence (BI) problem begins with modeling and analysis of business objectives and specifications, followed by a systematic derivation of real-time BI queries on warehouse data. In this position paper, we motivate the need for the development of a complete Real Time BI stack able to continuously evaluate and reason about strategic objectives. We argue that an integrated system, able to receive formal specifications of the organization's strategic objectives and to transform them into a set of queries that are continuously evaluated against the warehouse, offers significant benefits. In this context, we propose the development of a set of real-time query answering mechanisms able to identify warehouse segments with temporal patterns of special interest, as well as novel techniques for mining warehouse regions that represent expected, or unexpected threats and opportunities. With such a vision in mind, we propose an architecture for such a framework, and discuss relevant challenges and research directions.

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“…When the user ends the selection process, this component asks the DS Manager to start the computation of the data synopses on the basis of the selected attributes. Analysis Environment is the web-based application that allows users to define business indicators to be published in dashboards [25]. First, it loads ADAP metadata via the web service.…”

Section: Systemmentioning

confidence: 99%

Metadata for Approximate Query Answering Systems

Tria

Lefons

Tangorra

2012

Advances in Software Engineering

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In business intelligence systems, data warehouse metadata management and representation are getting more and more attention by vendors and designers. The standard language for the data warehouse metadata representation is the Common Warehouse Metamodel. However, business intelligence systems include also approximate query answering systems, since these software tools provide fast responses for decision making on the basis of approximate query processing. Currently, the standard meta-model does not allow to represent the metadata needed by approximate query answering systems. In this paper, we propose an extension of the standard metamodel, in order to define the metadata to be used in online approximate analytical processing. These metadata have been successfully adopted in ADAP, a web-based approximate query answering system that creates and uses statistical data profiles.

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Integrated model-driven dashboard development

Cited by 44 publications

References 14 publications

System Architecture for Approximate Query Processing

System Architecture for Approximate Query Processing

Strategic Management for Real-Time Business Intelligence

Metadata for Approximate Query Answering Systems

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