Towards Collaborative Multidimensional Query Recommendation with Triadic Association Rules

2015 ◽  
Vol 7 (3) ◽  
pp. 17-35 ◽  
Author(s):  
Sid Ali Selmane ◽  
Omar Boussaid ◽  
Fadila Bentayeb
Keyword(s):  
Association Rules ◽  
End Users ◽  
New Approach ◽  
Query Log ◽  
Log Files ◽  
Query Logs ◽  
On Line ◽  
Synthetic Datasets ◽  
Triadic Context

This paper describes a new personalization process for decisional queries through a new approach based on triadic association rules mining. This process exploits the decision query log files of end users and follows these five steps: (1) generation of a triadic context from the multidimensional query logs of OLAP1 query analysis server; (2) mapping the triadic context into the dyadic one; (3) computation of (conventional) dyadic association rules; (4) generation of triadic association rules through a factorization process of dyadic ones and convey a richer semantics. The aim of the personalization approach which is based on triadic rules is to recommend new decision queries to OLAP end users sharing some common properties. This paper aims at helping this class of users by recommending them personalized OLAP queries that they might use in their future OLAP sessions. To validate the approach, the authors developed a software prototype called P-TRIAR (Personalization based on TRIadic Association Rules) which extracts two types of triadic association rules from decision query log files. The first type of triadic rules will serve to the recommending queries by taking the collaborative aspect of OLAP users into account. The second type of triadic rules will enrich user queries. Preliminary experiments were conducted on both real and synthetic datasets to assess the quality of the recommendations in term of precision and recall measures, as well as the performance of their on-line computation.

“On line” collection quality of life (QL) items in the oncological hospital at home (OHH): A new approach

1993 ◽  
Vol 29 ◽  
pp. S215
Author(s):  
S. Glordani ◽  
C. Frenquelli ◽  
V. Paletta ◽  
S. Salvagni ◽  
F. Pannuti
Keyword(s):  
Quality Of Life ◽  
New Approach ◽  
On Line ◽  
Hospital At Home ◽  
At Home

scholarly journals Query Recommendations for OLAP Discovery-Driven Analysis

2011 ◽  
Vol 7 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Arnaud Giacometti ◽  
Patrick Marcel ◽  
Elsa Negre ◽  
Arnaud Soulet
Keyword(s):  
Open Source ◽  
Recommender System ◽  
Database Queries ◽  
Query Log ◽  
Levels Of Detail ◽  
On Line ◽  
A Current ◽  
Tedious Process

Recommending database queries is an emerging and promising field of research and is of particular interest in the domain of OLAP systems, where the user is left with the tedious process of navigating large datacubes. In this paper, the authors present a framework for a recommender system for OLAP users that leverages former users’ investigations to enhance discovery-driven analysis. This framework recommends the discoveries detected in former sessions that investigated the same unexpected data as the current session. This task is accomplished by (1) analysing the query log to discover pairs of cells at various levels of detail for which the measure values differ significantly, and (2) analysing a current query to detect if a particular pair of cells for which the measure values differ significantly can be related to what is discovered in the log. This framework is implemented in a system that uses the open source Mondrian server and recommends MDX queries. Preliminary experiments were conducted to assess the quality of the recommendations in terms of precision and recall, as well as the efficiency of their on-line computation.

A New Approach of Combined Shape and Gauge Control

2010 ◽  
Vol 145 ◽  
pp. 123-127
Author(s):  
Xiao Ping Zhang ◽  
Chang Jian Zhi ◽  
Bao Sun ◽  
Xiao Zhong Du ◽  
Jin Zhi Zhang
Keyword(s):  
Control Systems ◽  
Load Distribution ◽  
Shape Control ◽  
Control Method ◽  
Rolling Process ◽  
Distribution Method ◽  
New Approach ◽  
On Line ◽  
Strip Crown

Due to the coupled function of shape and gauge control in rolling process, mutual effects must be considered when shape control or gauge control is carried out. Traditional shape and gauge control theories are of independent and the control systems are also separated, so it is difficult to ensure the quality of shape and gauge simultaneously. Based on the updated shape theory and a single parameter model of load distribution method, a combined shape and gauge control method was developed, which can adjust rolling regulations on-line according to the deviation of strip crown measured at the exit of the rolling mill. The method can ensure gauge precision while strip crown is adjusted and the purpose of combined shape and gauge control is reached.

Query Recommendations for OLAP Discovery-Driven Analysis

2013 ◽  
pp. 66-90
Author(s):  
Arnaud Giacometti ◽  
Patrick Marcel ◽  
Elsa Negre ◽  
Arnaud Soulet
Keyword(s):  
Open Source ◽  
Recommender System ◽  
Database Queries ◽  
Query Log ◽  
Levels Of Detail ◽  
On Line ◽  
A Current ◽  
Tedious Process

Recommending database queries is an emerging and promising field of research and is of particular interest in the domain of OLAP systems, where the user is left with the tedious process of navigating large datacubes. In this paper, the authors present a framework for a recommender system for OLAP users that leverages former users’ investigations to enhance discovery-driven analysis. This framework recommends the discoveries detected in former sessions that investigated the same unexpected data as the current session. This task is accomplished by (1) analysing the query log to discover pairs of cells at various levels of detail for which the measure values differ significantly, and (2) analysing a current query to detect if a particular pair of cells for which the measure values differ significantly can be related to what is discovered in the log. This framework is implemented in a system that uses the open source Mondrian server and recommends MDX queries. Preliminary experiments were conducted to assess the quality of the recommendations in terms of precision and recall, as well as the efficiency of their on-line computation.

WiFiMon

2019 ◽  
Vol 8 (1) ◽  
pp. 1-18
Author(s):  
Christos Bouras ◽  
Kurt Baumann ◽  
Vasileios Kokkinos ◽  
Nikolaos Papachristos ◽  
Kostas Stamos
Keyword(s):  
Network Performance ◽  
Research Work ◽  
End Users ◽  
Clear Understanding ◽  
End User ◽  
Additional Information ◽  
Log Files ◽  
Measuring Network ◽  
Network Quality

Measuring network quality of a wireless network as experienced by end-users is quite difficult, as there is not a single tool available that can record measurements on all sides of the system. The approach presented in this research work is based on the end-user feedback, giving the opportunity of visualization of network performance in real time. This paper initially presents an overview of the developed tool, called WiFiMon, which has the ability to capture, record measurements and export statistics on the quality of Wi-Fi network as perceived by the end-users. The measurements are initiated by the end-users—without their intervention—after they visit a webpage or use a mobile application. WiFiMon aims to give a clear understanding of the Wi-Fi network conditions by measuring specific parameters of the network, such as download/upload throughput, and correlate these measurements with raw data from various log files to obtain additional information regarding the performance of specific access points. The results reveal the functionality of the proposed tool and its scalability.

Towards an Intelligent OLAP System Facing Sparse Problems

2014 ◽  
Vol 6 (4) ◽  
pp. 41-57
Author(s):  
Rania Koubaa ◽  
Eya Ben Ahmed ◽  
Faiez Gargouri
Keyword(s):  
Association Rules ◽  
Low Frequency ◽  
Decision Makers ◽  
Specific Class ◽  
Data Warehouses ◽  
New Approach ◽  
Useful Knowledge ◽  
Rare Association ◽  
Log Files ◽  
Sparse Problems

Exploring intelligent data stored in data warehouses may efficiently assist the knowledge-seeker in his decision process. Such traced information related to performed analysis by decision-makers on data warehouses are stored in OLAP log files. These files contain useful knowledge about the analysts' preferences. Sometimes, some formulated queries provide no results. Such a dilemma is known as the sparsity problem. In this paper, to overcome this limitation in user-centric data warehouses, the authors focus on a specific class of preferences, namely the conflicting preferences. Indeed, a conflicting preference describes a low frequency preference stored in OLAP log files, so that it is considered as tailored to given analysts. Such preferences are characterized by their rarity. To deal with this issue, the authors introduce a new approach to discover these preferences through mining of rare association rules using a new introduced method for generating the N highest confidence rare association rules. The derived rare preferences will be used to reformulate the launched query avoiding an empty result. The carried out experiments on their built online recruitment data warehouse point out the efficiency of their approach.

Automatic buffer capacity based sensor for effluent quality monitoring

1996 ◽  
Vol 33 (1) ◽  
pp. 81-87
Author(s):  
L. Van Vooren ◽  
P. Willems ◽  
J. P. Ottoy ◽  
G. C. Vansteenkiste ◽  
W. Verstraete
Keyword(s):  
Wastewater Treatment ◽  
Buffer Capacity ◽  
Wastewater Treatment Plants ◽  
Data Interpretation ◽  
Full Scale ◽  
Quality Monitoring ◽  
Effluent Quality ◽  
Capacity Curves ◽  
On Line

The use of an automatic on-line titration unit for monitoring the effluent quality of wastewater plants is presented. Buffer capacity curves of different effluent types were studied and validation results are presented for both domestic and industrial full-scale wastewater treatment plants. Ammonium and ortho-phosphate monitoring of the effluent were established by using a simple titration device, connected to a data-interpretation unit. The use of this sensor as the activator of an effluent quality proportional sampler is discussed.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

Guideline for the management of long tunnelled external ventricular drains in chronic hydrocephalus

2021 ◽  
Vol 30 (7) ◽  
pp. 416-421
Author(s):  
Phillip Correia Copley ◽  
John Emelifeonwu ◽  
Pasquale Gallo ◽  
Drahoslav Sokol ◽  
Jothy Kandasamy ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Nursing Care ◽  
Pilocytic Astrocytoma ◽  
Parental Education ◽  
Chronic Hydrocephalus ◽  
Treatment Protocols ◽  
New Approach ◽  
Viable Treatment

This article reports on the journey of a child with an inoperable hypothalamic-origin pilocytic astrocytoma causing hydrocephalus, which was refractory to treatment with shunts, and required a new approach. With multidisciplinary support, excellent nursing care and parental education, the child's hydrocephalus was managed long term in the community with bilateral long-tunnelled external ventricular drains (LTEVDs). This article describes the patient's journey and highlights the treatment protocols that were created to achieve this feat. Despite the difficulties in initially setting up these protocols, they proved successful and thus the team managing the patient proposed that LTEVDs are a viable treatment option for children with hydrocephalus in the context of inoperable tumours to help maximise quality of life.

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