Self Maintenance of Materialized XQuery Views via Query Containment and Re-Writing

In recent years XML, the eXtensible Markup Language has become the de-facto standard for publishing and exchanging information on the web and in enterprise data integration systems. Materialized views are often used in information integration systems to present a unified schema for efficient querying of distributed and possibly heterogenous data sources. On similar lines, ACE-XQ, an XQuery based semantic caching system shows the significant performance gains achieved by caching query results (as materialized views) and using these materialized views along with query containment techniques for answering future queries over distributed XML data sources. To keep data in these materialized views of ACE-XQ up-to-date, the view must be maintained i.e. whenever the base data changes, the corresponding cached data in the materialized view must also be updated. This thesis builds on the query containment ideas of ACE-XQ and proposes an efficient approach for self-maintenance of materialized views. Our experimental results illustrate the significant performance improvement achieved by this strategy over view re-computation for a variety of situations

Mining interesting events on large and dynamic data

Nowadays, almost every human interaction produces some form of data. These data are available either to every user, e.g.~images uploaded on Flickr or to users with specific privileges, e.g.~transactions in a bank. The huge amount of these produced data can easily overwhelm humans that try to make sense out of it. The need for methods that will analyse the content of the produced data, identify emerging topics in it and present the topics to the users has emerged. In this work, we focus on emerging topics identification over large and dynamic data. More specifically, we analyse two types of data: data published in social networks like Twitter, Flickr etc.~and structured data stored in relational databases that are updated through continuous insertion queries. In social networks, users post text, images or videos and annotate each of them with a set of tags describing its content. We define sets of co-occurring tags to represent topics and track the correlations of co-occurring tags over time. We split the tags to multiple nodes and make each node responsible of computing the correlations of its assigned tags. We implemented our approach in Storm, a distributed processing engine, and conducted a user study to estimate the quality of our results. In structured data stored in relational databases, top-k group-by queries are defined and an emerging topic is considered to be a change in the top-k results. We maintain the top-k result sets in the presence of updates minimising the interaction with the underlying database. We implemented and experimentally tested our approach.Heutzutage entstehen durch fast jede menschliche Aktion und Interaktion Daten. Fotos werden auf Flickr bereitgestellt, Neuigkeiten über Twitter verbreitet und Kontakte in Linkedin und Facebook verwaltet; neben traditionellen Vorgängen wie Banktransaktionen oder Flugbuchungen, die Änderungen in Datenbanken erzeugen. Solch eine riesige Menge an Daten kann leicht überwältigend sein bei dem Versuch die Essenz dieser Daten zu extrahieren. Neue Methoden werden benötigt, um Inhalt der Daten zu analysieren, neu entstandene Themen zu identifizieren und die so gewonnenen Erkenntnisse dem Benutzer in einer übersichtlichen Art und Weise zu präsentieren. In dieser Arbeit werden Methoden zur Identifikation neuer Themen in großen und dynamischen Datenmengen behandelt. Dabei werden einerseits die veröffentlichten Daten aus sozialen Netzwerken wie Twitter und Flickr und andererseits strukturierte Daten aus relationalen Datenbanken, welche kontinuierlich aktualisiert werden, betrachtet. In sozialen Netzwerken stellen die Benutzer Texte, Bilder oder Videos online und beschreiben diese für andere Nutzer mit Schlagworten, sogenannten Tags. Wir interpretieren Gruppen von zusammen auftretenden Tags als eine Art Thema und verfolgen die Beziehung bzw. Korrelation dieser Tags über einen gewissen Zeitraum. Abrupte Anstiege in der Korrelation werden als Hinweis auf Trends aufgefasst. Die eigentlich Aufgabe, das Zählen von zusammen auftretenden Tags zur Berechnung von Korrelationsmaßen, wird dabei auf eine Vielzahl von Computerknoten verteilt. Die entwickelten Algorithmen wurden in Storm, einem neuartigen verteilten Datenstrommanagementsystem, implementiert und bzgl. Lastbalancierung und anfallender Netzwerklast sorgfältig evaluiert. Durch eine Benutzerstudie wird darüber hinaus gezeigt, dass die Qualität der gewonnenen Trends höher ist als die Qualität der Ergebnisse bestehender Systeme. In strukturierten Daten von relationalen Datenbanksystemen werden Beste-k Ergebnislisten durch Aggregationsanfragen in SQL definiert. Interessant dabei sind eintretende Änderungen in diesen Listen, was als Ereignisse (Trends) aufgefasst wird. In dieser Arbeit werden Methoden präsentiert diese Ergebnislisten möglichst effizient instand zu halten, um Interaktionen mit der eigentlichen Datenbank zu minimieren

Mining interesting events on large and dynamic data

Nowadays, almost every human interaction produces some form of data. These data are available either to every user, e.g.~images uploaded on Flickr or to users with specific privileges, e.g.~transactions in a bank. The huge amount of these produced data can easily overwhelm humans that try to make sense out of it. The need for methods that will analyse the content of the produced data, identify emerging topics in it and present the topics to the users has emerged. In this work, we focus on emerging topics identification over large and dynamic data. More specifically, we analyse two types of data: data published in social networks like Twitter, Flickr etc.~and structured data stored in relational databases that are updated through continuous insertion queries. In social networks, users post text, images or videos and annotate each of them with a set of tags describing its content. We define sets of co-occurring tags to represent topics and track the correlations of co-occurring tags over time. We split the tags to multiple nodes and make each node responsible of computing the correlations of its assigned tags. We implemented our approach in Storm, a distributed processing engine, and conducted a user study to estimate the quality of our results. In structured data stored in relational databases, top-k group-by queries are defined and an emerging topic is considered to be a change in the top-k results. We maintain the top-k result sets in the presence of updates minimising the interaction with the underlying database. We implemented and experimentally tested our approach.Heutzutage entstehen durch fast jede menschliche Aktion und Interaktion Daten. Fotos werden auf Flickr bereitgestellt, Neuigkeiten über Twitter verbreitet und Kontakte in Linkedin und Facebook verwaltet; neben traditionellen Vorgängen wie Banktransaktionen oder Flugbuchungen, die Änderungen in Datenbanken erzeugen. Solch eine riesige Menge an Daten kann leicht überwältigend sein bei dem Versuch die Essenz dieser Daten zu extrahieren. Neue Methoden werden benötigt, um Inhalt der Daten zu analysieren, neu entstandene Themen zu identifizieren und die so gewonnenen Erkenntnisse dem Benutzer in einer übersichtlichen Art und Weise zu präsentieren. In dieser Arbeit werden Methoden zur Identifikation neuer Themen in großen und dynamischen Datenmengen behandelt. Dabei werden einerseits die veröffentlichten Daten aus sozialen Netzwerken wie Twitter und Flickr und andererseits strukturierte Daten aus relationalen Datenbanken, welche kontinuierlich aktualisiert werden, betrachtet. In sozialen Netzwerken stellen die Benutzer Texte, Bilder oder Videos online und beschreiben diese für andere Nutzer mit Schlagworten, sogenannten Tags. Wir interpretieren Gruppen von zusammen auftretenden Tags als eine Art Thema und verfolgen die Beziehung bzw. Korrelation dieser Tags über einen gewissen Zeitraum. Abrupte Anstiege in der Korrelation werden als Hinweis auf Trends aufgefasst. Die eigentlich Aufgabe, das Zählen von zusammen auftretenden Tags zur Berechnung von Korrelationsmaßen, wird dabei auf eine Vielzahl von Computerknoten verteilt. Die entwickelten Algorithmen wurden in Storm, einem neuartigen verteilten Datenstrommanagementsystem, implementiert und bzgl. Lastbalancierung und anfallender Netzwerklast sorgfältig evaluiert. Durch eine Benutzerstudie wird darüber hinaus gezeigt, dass die Qualität der gewonnenen Trends höher ist als die Qualität der Ergebnisse bestehender Systeme. In strukturierten Daten von relationalen Datenbanksystemen werden Beste-k Ergebnislisten durch Aggregationsanfragen in SQL definiert. Interessant dabei sind eintretende Änderungen in diesen Listen, was als Ereignisse (Trends) aufgefasst wird. In dieser Arbeit werden Methoden präsentiert diese Ergebnislisten möglichst effizient instand zu halten, um Interaktionen mit der eigentlichen Datenbank zu minimieren

Contextual Analysis of Large-Scale Biomedical Associations for the Elucidation and Prioritization of Genes and their Roles in Complex Disease

Vast amounts of biomedical associations are easily accessible in public resources, spanning gene-disease associations, tissue-specific gene expression, gene function and pathway annotations, and many other data types. Despite this mass of data, information most relevant to the study of a particular disease remains loosely coupled and difficult to incorporate into ongoing research. Current public databases are difficult to navigate and do not interoperate well due to the plethora of interfaces and varying biomedical concept identifiers used. Because no coherent display of data within a specific problem domain is available, finding the latent relationships associated with a disease of interest is impractical. This research describes a method for extracting the contextual relationships embedded within associations relevant to a disease of interest. After applying the method to a small test data set, a large-scale integrated association network is constructed for application of a network propagation technique that helps uncover more distant latent relationships. Together these methods are adept at uncovering highly relevant relationships without any a priori knowledge of the disease of interest. The combined contextual search and relevance methods power a tool which makes pertinent biomedical associations easier to find, easier to assimilate into ongoing work, and more prominent than currently available databases. Increasing the accessibility of current information is an important component to understanding high-throughput experimental results and surviving the data deluge

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Professor/practitioner case development program - 1994 case studies

https://egrove.olemiss.edu/aicpa_guides/1340/thumbnail.jp

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance