Data Warehousing. Seminar im Sommersemester 1996

Sich verstaerkender Wettbewerbsdruck und raschveraenderliche Maerkte erfordern schnell verfuegbare, umfassende und hochwertige Informatio- nen fuer die Entscheidungsunterstuetzung. Ein wesentlicher Baustein eines Unternehmens--Informationssystems ist das Data Warehouse als eine integrierte Sicht auf die geschaeftsrelevanten Unternehmensdaten. Durch Data Mining-- und OLAP--Techniken wird dieses Informationssystem zu einer Entscheidungsunterstuetzungsumgebung ausgebaut. Dieser Bericht umfasst die Ausarbeitungen des im Sommersemester 1996 an der Fakultaet fuer Informatik durchgefuehrten Seminars zu den wesentlichen, mit dem Thema Data Warehousing verbundenen Konzepten und Techniken

Capital stranding cascades: The impact of decarbonisation on productive asset utilisation

This article develops a novel methodological framework to investigate the exposure of eco- nomic systems to the risk of physical capital stranding. Combining Input-Output (IO) and network theory, we define measures to identify both the sectors likely to trigger relevant capital stranding cascades and those most exposed to capital stranding risk. We show how, in a sample of ten European countries, mining is among the sectors with the highest external asset strand- ing multipliers. The sectors most affected by capital stranding triggered by decarbonisation include electricity and gas; coke and refined petroleum products; basic metals; and transporta- tion. From these sectors, stranding would frequently cascade down to chemicals; metal products; motor vehicles water and waste services; wholesale and retail trade; and public administration. Finally, we provide an estimate for the lower-bound amount of assets at risk of transition-related stranding, which is in the range of 0.6-8.2% of the overall productive capital stock for our sample of countries, mainly concentrated in the electricity and gas sector, manufacturing, and mining. These results confirm the systemic relevance of transition-related risks on European societies.Series: Ecological Economic Paper

Warehousing and Analyzing Streaming Data Quality Information

The development of integrative IS architectures focuses typically on solving problems related to the functionality of the system. It is attempted to design optimally flexible interfaces that can achieve the most agile architecture. The quality of the data that will be exchanged across these interfaces is often disregarded (implicitly or explicitly). This results in distributed applications which are functionally correct but cannot be deployed due to the low quality of the data involved. In order to avoid wrong business decisions due to ‘dirty data’, quality characteristics have to be captured, processed, and provided to the respective business task. However, the issue of how to efficiently provide applications with information about data quality is still an open research problem. Our approach tackles the problems posed by data quality deficiencies by presenting a novel concept to stream and warehouse data together with its describing data quality information

DATA WAREHOUSE AND BUSINESS INTELLIGENCE STRATEGIES AND TRENDS

In recent decades following the evolution of information technology, decision support systems have played an important role by presenting the necessary information resulted from the operational systems processes. By continuing improvement of the methods as well as the contribution of technological advance the applicability of decision support systems is now generalized and has reached the status of complex systems of business intelligence. Business Intelligence is about creating intelligence about a business based on a cyclic flow which consists of capturing, analyzing, planning and implementation resulting in streamlining the organization.Decisions, DSS, Data Driven, Business Intelligence, Data Warehouse

BPM News - Folge 2

Schwerpunkt dieser Kolumne bildet das Thema Business Process Intelligence (BPI), das sowohl auf Fachkonferenzen als auch im kommerziellen Umfeld (z. B. ARIS Toolset, IBM Websphere Business Integration Monitor) vermehrt ins Blickfeld geraten ist. Business Process Intelligence subsumiert Konzepte, Anwendungen und Werkzeuge für die Erfassung, Auswertung und Visualisierung von Prozessechtdaten (inkl. Process Mining). In diesem Zusammenhang wurden in 2005 erstmalig auch zwei Workshops ausgerichtet, die wir in dieser Kolumne kurz zusammenfassen werden

Aktuelles Schlagwort: Business Process Intelligence

In jüngerer Vergangenheit rückt vermehrt die Erfassung und Analyse von Prozessechtdaten (z.B. zum Start und Ende von Prozessaktivitäten) in den Blickpunkt. Solche Daten werden von den meisten prozessorientierten Informationssystemen geliefert. Das Schlagwort Business Process Intelligence (BPI) subsumiert existierende Konzepte, Anwendungen und Werkzeuge für die Erfassung und Auswertung von Prozessdaten. Anwendungsbeispiele sind die aggregierte Auswertung von Daten über einer Kollektion von Prozessinstanzen (z.B. mittlere Prozessdurchlaufzeit oder durchschnittliche Ressourcenallokation) oder die automatische Ableitung von Prozessmodellen aus den Protokolldaten zu abgeschlossenen Prozessausführungen. Aus Informatiksicht gilt es, geeignete Rahmenbedingungen zur Unterstützung entsprechender Anwendungsszenarien zu schaffen. Wichtige Aspekte, die in diesem Beitrag in einer BPI-Referenzarchitektur eingeordnet werden, betreffen die Datenintegration, das Prozess-Monitoring und die Prozessvisualisierung

Limits or Integration? – Manufacturing Execution Systems and Operational Business Intelligence

Manufacturing Execution Systems (MES) and Operational Business Intelligence (OpBI) analyze and control operationalactivities in different organizational application fields. This raises the question how far these concepts are interrelated incontext of company-wide process coordination and analysis. The goal of this paper is the evaluation and conceptualclassification of MES and OpBI to base subsequent research actions. A literature review is conducted to recognize if arelationship of the concepts is taken into account in academics and to look for research gaps. Therefore, a representativenumber of articles have been extracted from selected scientific databases. The review results in four publications illuminatingonly single correlation aspects. This leads to the conclusion that further research in context of MES and OpBI is needed

Fundamentals, Misvaluation, and Investment. The Real Story

Is real investment fully determined by fundamentals or is it sometimes affected by stockmarket misvaluation? We introduce three new tests that: measure the reaction of investment to sales shocks for firms that may be overvalued; use Fama-MacBeth regressions to determine whether "overinvestment" affects subsequent returns; and analyze the time path of the marginal product of capital in reaction to fundamental and misvaluation shocks. Besides these qualitative tests, we introduce a measure of misvaluation into standard investment equations to estimate the quantitative effect of misvaluation on investment. Overall, the evidence suggests that both fundamental and misvaluation shocks affect investment.Investment, Stock market, Fundamentals, Misvaluation, Bubbles, Real effects of financial markets