Large-scale database modeling: Developing XML schema

This thesis is concerned with the team efforts to develop a large database to track medical information. A large XML schema is developed from the Extended ER diagram to capture key and foreign key constraints. The strong types of XML schema were also used to assert patterns and domain constraints. These constraints will be used to aid the recognition of medical forms

Comparing temporal behavior of fast objective video quality measures on a large-scale database

In many application scenarios, video quality assessment is required to be fast and reasonably accurate. The characterisation of objective algorithms by subjective assessment is well established but limited due to the small number of test samples. Verification using large-scale objectively annotated databases provides a complementary solution. In this contribution, three simple but fast measures are compared regarding their agreement on a large-scale database. In contrast to subjective experiments, not only sequence-wise but also framewise agreement can be analyzed. Insight is gained into the behavior of the measures with respect to 5952 different coding configurations of High Efficiency Video Coding (HEVC). Consistency within a video sequence is analyzed as well as across video sequences. The results show that the occurrence of discrepancies depends mostly on the configured coding structure and the source content. The detailed observations stimulate questions on the combined usage of several video quality measures for encoder optimization

Fault-Tolerant Partial Replication in Large-Scale Database Systems

We investigate a decentralised approach to committing transactions in a replicated database, under partial replication. Previous protocols either re-execute transactions entirely and/or compute a total order of transactions. In contrast, ours applies update values, and orders only conflicting transactions. It results that transactions execute faster, and distributed databases commit in small committees. Both effects contribute to preserve scalability as the number of databases and transactions increase. Our algorithm ensures serializability, and is live and safe in spite of faults

Large-scale database modeling: Discovering attributes, entities, and relationships

This thesis is concerned with the team efforts to develop a, large database to track medical information. Entity relational model approach is taken to study an extensive set of forms for structure discovery. This approach has led to thousands of attributes and hundreds of entities and relationships. A meta-database is used to manipulate this data for further design

Large-scale database modeling: Extended Er diagrams and Uml

This thesis is concerned with the team efforts to develop a large database to track medical information. An Extended Entity Relationship diagram is developed using UML notation to describe the design of the database. Special attention was given for features of EER diagram which can not easily be represented by ER diagram

SoccerDB: A Large-Scale Database for Comprehensive Video Understanding

Soccer videos can serve as a perfect research object for video understanding because soccer games are played under well-defined rules while complex and intriguing enough for researchers to study. In this paper, we propose a new soccer video database named SoccerDB, comprising 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks. To our knowledge, it is the largest database for comprehensive sports video understanding on various aspects. We further survey a collection of strong baselines on SoccerDB, which have demonstrated state-of-the-art performances on independent tasks. Our evaluation suggests that we can benefit significantly when jointly considering the inner correlations among those tasks. We believe the release of SoccerDB will tremendously advance researches around comprehensive video understanding. {\itshape Our dataset and code published on https://github.com/newsdata/SoccerDB.}Comment: accepted by MM2020 sports worksho

Database Engines for Geographical Information Systems

Our ability to identify, acquire, store, enquire on and analyse data is increasing as never before, especially in the GIS field. Technologies are becoming available to manage a wider variety of data and to make intelligent inferences on that data. The mainstream arrival of large-scale database engines is not far away. The experience of using the first such products tells us that they will radically change data management in the GIS field

Recognisation of Outlier using Distance based method for Large Scale Database

This paper studies the difficulties of outlier detection on inexact data. We study the normal instances for each uncertain object using the instances of objects with analogous properties. Outlier detection is a significant research problem in data mining that goals to determine valuable abnormal and irregular patterns hidden in vast data sets. Most existing outlier detection approaches only deal with static data with comparatively low dimensionality. Newly, outlier detection for high-dimensional stream data turn into a new emergent research problem. A key remark that inspires this research is that outliers in high-dimensional data are predictable outliers, i.e., they are embedded in lower dimensional subspaces. Detecting projected outliers from high-dimensional stream data is a very stimulating task for numerous reasons. The paper shows the detailed study of outlier detection algorithms and its results also