Visualising Temporal Item Sets: Guided Drill-Down with Hierarchical Attributes

2013 ◽  
pp. 533-541 ◽  
Author(s):  
Fabian Schmidt ◽  
Martin Spott
Keyword(s):  
Drill Down

Deep drill-down analysis for failures detection in the production line

2021 ◽  
Author(s):  
Martin Nemeth ◽  
Dmitrii Borkin ◽  
Andrea Nemethova ◽  
German Michalconok
Keyword(s):  
Production Line ◽  
Drill Down

Drill Down Into Your Donor Pyramid

2019 ◽  
Vol 27 (9) ◽  
pp. 7-7
Author(s):  
Daniel Lindley
Keyword(s):  
Drill Down

An Experimental Study of Multidimensional Hierarchical Accounting Data: Drill-Down Paths Can Influence Economic Decisions

2007 ◽  
Vol 21 (2) ◽  
pp. 69-86 ◽  
Author(s):  
Jacob Peng ◽  
Ralph E. Viator ◽  
Steve Buchheit
Keyword(s):  
Predictive Ability ◽  
Performance Outcomes ◽  
Decision Makers ◽  
Monetary Incentives ◽  
Aggregate Level ◽  
Cognitive Fit ◽  
Accounting Data ◽  
Economic Decisions ◽  
Level Performance ◽  
Drill Down

Although decision support systems utilizing multidimensional hierarchical data have rightfully been praised for their ability to enhance decision making, we find that the drill-down path offered by such systems can influence economic decisions—sometimes in a suboptimal fashion. Our experimental investigation offers profitmaximizing monetary incentives to decision makers who navigate a simple multidimensional system. Specifically, decision makers view three possible drill-down paths that each contain three lower-level outcomes of subunit performance (i.e., only nine possible outcomes exist). We manipulate the predictive ability of aggregate data by changing the system-offered drill-down path. In our experiment, we keep all numeric performance outcomes constant; however, half of the time, the optimal outcome lies within the best aggregate level performer and half the time it does not. We find economic decisions are significantly worse when aggregate level performance fails to predict the optimal lower-level performance outcome. We also find that reducing decision effort via proper cognitive fit improves economic decisions.

Space-Time Roll-up and Drill-down into Geo-Trend Stream Cubes

2011 ◽  
pp. 365-375 ◽  
Author(s):  
Anna Ciampi ◽  
Annalisa Appice ◽  
Donato Malerba ◽  
Angelo Muolo
Keyword(s):  
Space Time ◽  
Drill Down

Why Do Lessons Learned Often Fail?

2021 ◽  
pp. 176-193
Author(s):  
Ian Fry
Keyword(s):  
Learning Process ◽  
Real World ◽  
Lessons Learned ◽  
The Real ◽  
Drill Down

Organisations know they should do lessons learned. Standards like ISO9001 and ISO30401 say they should. Many try; few succeed. Traditionally, the first answer to the question is “lessons were observed, but not learned,” which reflects meaningful action was not taken as a result of the reported lesson. A lesson may have been identified, but nothing changed. As a result, learning did not happen. So why is this so? It is important to identify the ways in which the process towards effective lesson learning is becoming lost within the stages and how knowledge practitioners and those responsible for lessons learned can best help. This chapter will attempt to drill down on this answer, concentrating on the processes deployed and the real-world issues around the lesson-learning process.

Constraint-Based Multi-Dimensional Databases

2009 ◽  
pp. 961-986
Author(s):  
Franck Ravat ◽  
Olivier Teste ◽  
Gilles Zurfluh
Keyword(s):  
Graphical Representation ◽  
Query Language ◽  
Computer Scientist ◽  
Data Querying ◽  
Definition Of ◽  
Multidimensional Modelling ◽  
Graphical Query Language ◽  
Drill Down

This chapter deals with constraint-based multidimensional modelling. The model we define integrates a constellation of facts and dimensions. Along each dimension, various hierarchies are possibly defined and the model supports multiple instantiations of dimensions. The main contribution is the definition of intra-dimension constraints between hierarchies of a same dimension as well as inter-dimension constraints of various dimensions. To facilitate data querying, we define a multi-dimensional query algebra, which integrates the main multi-dimensional operators such as rotations, drill down, roll up… These operators support the constraint-based multi-dimensional modelling. Finally, we present two implementations of this algebra. First, OLAP-SQL is a textual language integrating multi-dimensional concepts (fact, dimension, hierarchy), but it is based on classical SQL syntax. This language is dedicated to specialists such as multi-dimensional database administrators. Second, a graphical query language is presented. This language consists in a graphical representation of multi-dimensional databases, and users specify directly their queries over this graph. This approach is dedicated to non-computer scientist users.

Constraint-Based Multi-Dimensional Databases

2011 ◽  
pp. 323-360 ◽  
Author(s):  
Franck Ravat ◽  
Olivier Teste ◽  
Gilles Zurfluh
Keyword(s):  
Graphical Representation ◽  
Query Language ◽  
Computer Scientist ◽  
Data Querying ◽  
Definition Of ◽  
Graphical Query Language ◽  
Drill Down

This chapter deals with constraint-based multi-dimensional modelling. The model we define integrates a constellation of facts and dimensions. Along each dimension, various hierarchies are possibly defined and the model supports multiple instantiations of dimensions. The main contribution is the definition of intra-dimension constraints between hierarchies of a same dimension as well as inter-dimension constraints of various dimensions. To facilitate data querying, we define a multi-dimensional query algebra, which integrates the main multi-dimensional operators such as rotations, drill down, roll up… These operators support the constraint-based multi-dimensional modelling. Finally, we present two implementations of this algebra. First, OLAP-SQL is a textual language integrating multi-dimensional concepts (fact, dimension, hierarchy), but it is based on classical SQL syntax. This language is dedicated tospecialists such as multi-dimensional database administrators. Second, a graphical query language is presented. This language consists in a graphical representation of multi-dimensional databases, and users specify directly their queries over this graph. This approach is dedicated to non-computer scientist users.

Sign in / Sign up

Export Citation Format

Share Document