A holistic scalability strategy for time series databases following cascading polyglot persistence

Time series databases aim to handle big amounts of data in a fast way, both when introducing new data to the system, and when retrieving it later on. However, depending on the scenario in which these databases participate, reducing the number of requested resources becomes a further requirement. Following this goal, NagareDB and its Cascading Polyglot Persistence approach were born. They were not just intended to provide a fast time series solution, but also to find a great cost-efficiency balance. However, although they provided outstanding results, they lacked a natural way of scaling out in a cluster fashion. Consequently, monolithic approaches could extract the maximum value from the solution but distributed ones had to rely on general scalability approaches. In this research, we proposed a holistic approach specially tailored for databases following Cascading Polyglot Persistence to further maximize its inherent resource-saving goals. The proposed approach reduced the cluster size by 33%, in a setup with just three ingestion nodes and up to 50% in a setup with 10 ingestion nodes. Moreover, the evaluation shows that our scaling method is able to provide efficient cluster growth, offering scalability speedups greater than 85% in comparison to a theoretically 100% perfect scaling, while also ensuring data safety via data replication.This research was partly supported by the Grant Agreement No. 857191, by the Spanish Ministry of Science and Innovation (contract PID2019-107255GB) and by the Generalitat de Catalunya (contract 2017-SGR-1414).Peer ReviewedPostprint (published version

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Database architecture evolution: Mammals flourished long before dinosaurs became extinct

The holy grail for database architecture research is to find a solution that is Scalable & Speedy, to run on anything from small ARM processors up to globally distributed compute clusters, Stable & Secure, to service a broad user community, Small & Simple, to be comprehensible to a small team of programmers, Self-managing, to let it run out-of-the-box without hassle. In this paper, we provide a trip report on this quest, covering both past experiences, ongoing research on hardware-conscious algorithms, and novel ways towards self-management specifically focused on column store solutions

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

An evaluation of non-relational database management systems as suitable storage for user generated text-based content in a distributed environment

Non-relational database management systems address some of the limitations relational database management systems have when storing large volumes of unstructured, user generated text-based data in distributed environments. They follow different approaches through the data model they use, their ability to scale data storage over distributed servers and the programming interface they provide. An experimental approach was followed to measure the capabilities these alternative database management systems present in their approach to address the limitations of relational databases in terms of their capability to store unstructured text-based data, data warehousing capabilities, ability to scale data storage across distributed servers and the level of programming abstraction they provide. The results of the research highlighted the limitations of relational database management systems. The different database management systems do address certain limitations, but not all. Document-oriented databases provide the best results and successfully address the need to store large volumes of user generated text-based data in a distributed environmentSchool of ComputingM. Sc. (Computer Science

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

The XFM view adaptation mechanism: An essential component for XML data warehouses

In the past few years, with many organisations providing web services for business and communication purposes, large volumes of XML transactions take place on a daily basis. In many cases, organisations maintain these transactions in their native XML format due to its flexibility for xchanging data between heterogeneous systems. This XML data provides an important resource for decision support systems. As a consequence, XML technology has slowly been included within decision support systems of data warehouse systems. The problem encountered is that existing native XML database systems suffer from poor performance in terms of managing data volume and response time for complex analytical queries. Although materialised XML views can be used to improve the performance for XML data warehouses, update problems then become the bottleneck of using materialised views. Specifically, synchronising materialised views in the face of changing view definitions, remains a significant issue. In this dissertation, we provide a method for XML-based data warehouses to manage updates caused by the change of view definitions (view redefinitions), which is referred to as the view adaptation problem. In our approach, views are defined using XPath and then modelled using a set of novel algebraic operators and fragments. XPath views are integrated into a single view graph called the XML Fragment Materialisation (XFM) View Graph, where common parts between different views are shared and appear only once in the graph. Fragments within the view graph can be selected for materialisation to facilitate the view adaptation process. While changes are applied, our view adaptation algorithms can quickly determine what part of the XFM view graph is affected. The adaptation algorithms then perform a structural adaptation to update the view graph, followed by data adaptation to update materialised fragments

Development of new data partitioning and allocation algorithms for query optimization of distributed data warehouse systems

Distributed databases and in particular distributed data warehousing are becoming an increasingly important technology for information integration and data analysis. Data Warehouse (DW) systems are used by decision makers for performance measurement and decision support. However, although data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, the OLAP query response time is strongly affected by the volume of data need to be accessed from storage disks. Data partitioning is one of the physical design techniques that may be used to optimize query processing cost in DWs. It is a non redundant optimization technique because it does not replicate data, contrary to redundant techniques like materialized views and indexes. The warehouse partitioning problem is concerned with determining the set of dimension tables to be partitioned and using them to generate the fact table fragments. In this work an enhanced grouping algorithm that avoids the limitations of some existing vertical partitioning algorithms is proposed. Furthermore, a static partitioning algorithm that allows fragmentation at early stages of schema design is presented. The thesis also, investigates the performance of the data warehouse after implementing a combination of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques to horizontally partition the data warehouse star schema. It, then presents the experimentation and implementation results of the proposed algorithm. This research presented different approaches to optimize data fragments allocation cost using a greedy mathematical model and a combination of simulated annealing and genetic algorithm to determine the site by site allocation leading to optimal solutions for fragments distribution. Throughout this thesis, the term fragmentation and partitioning will be used interchangeably