Tengu: An Experimentation Platform for Big Data Applications

Vanhove, Thomas; Seghbroeck, Gregory Van; Wauters, Tim; Turck, Filip De; Vermeulen, Brecht; Demeester, Piet

doi:10.1109/icdcsw.2015.19

Cited by 10 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Tengu experimentation platform [30] is running on the Virtual Wall testbeds, making it possible to automatically deploy several data processing, storage and cloud technologies, turning this process agile and straightforward. For the evaluation of this proof of concept 10 third generation nodes were used: 2x Hexacore Intel E5645 (2.4GHz) CPU, 24GB RAM, 1x 250GB hard disk, 1-5 gigabit nics.…”

Section: A Experiments Set Upmentioning

confidence: 99%

Interactive querying and data visualization for abuse detection in social network sites

Ordonez-Ante

Vanhove

Seghbroeck

et al. 2016

2016 11th International Conference for Internet Technology and Secured Transactions (ICITST)

Self Cite

View full text Add to dashboard Cite

Abstract-Big Data technologies have traditionally operated in an offline setting, collecting large batches of information on clusters of commodity machines and performing complex and time-consuming computations over it. While frameworks following this approach served well for most applications involving big data analysis during the last decade, other use cases have recently emerged posing challenging requirements on latency and demanding real-time data processing, querying and visualization. That is the case for applications aiming at detecting threatening behaviors in social network platforms, where timely action is required to avoid adverse consequences. In this sense, more and more attention has been drawn towards online data processing systems claiming to address the limitations of batch-oriented frameworks. This paper reports a work in progress on distributed data processing for enabling low-latency querying over big data sets. Two software architectures are discussed for addressing the problem and an experimental evaluation is performed on a proof of concept implementation showing how an approach based on query pre-processing and stateful distributed stream computation can meet the requirements for supporting interactive querying on large and continuously generated data.

show abstract

Section: A Experiments Set Upmentioning

confidence: 99%

Interactive querying and data visualization for abuse detection in social network sites

Ordonez-Ante

Vanhove

Seghbroeck

et al. 2016

2016 11th International Conference for Internet Technology and Secured Transactions (ICITST)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The CoT framework has been deployed within a Smart City testbed in Antwerp, Belgium [6] and is based on the Tengu platform [7], an automated orchestration service for designing big data frameworks and on LimeDS [8], a toolkit designed for the rapid development of data-driven services. Both technologies provide scalable and flexible resource allocation operations, which allow researchers and developers to design and deploy specific IoT service models.…”

Section: Introductionmentioning

confidence: 99%

City of Things: Enabling Resource Provisioning in Smart Cities

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract-In the last years, traffic over wireless networks has been increasing exponentially due to the impact of Internet of Things (IoT). IoT is transforming a wide range of services in different domains of urban life, such as environmental monitoring, home automation and public transportation. The so-called Smart City applications will introduce a set of stringent requirements, such as low latency and high mobility, since services must be allocated and instantiated on-demand simultaneously close to multiple devices at different locations. Efficient resource provisioning functionalities are needed to address these demanding constraints introduced by Smart City applications while minimizing resource costs and maximizing Quality of Service (QoS). In this article, the City of Things (CoT) framework is presented, which provides not only data collection and analysis functionalities but also automated resource provisioning mechanisms for future Smart City applications. CoT is deployed as a Smart City testbed in Antwerp (Belgium) that allows researchers and developers to easily setup and validate IoT experiments. A Smart City use case based on Air Quality Monitoring through the deployment of air quality sensors in moving cars has been presented showing the full applicability of the CoT framework for a flexible and scalable resource provisioning in the Smart City ecosystem.

show abstract

“…The proposed solution aims to tackle the issues concerning polyglot persistence, ie, enable applications to access and store data in different data stores simultaneously and allow for dynamic changeovers between supported data stores based on monitoring information or the intervention of the customer or administrator. The proposed solution makes use of a new open source Platform‐as‐a‐Service (PaaS) called Tengu . The Tengu platform provides researchers a time‐saving approach for building big data analysis frameworks through automated installation, configuration, and integration of big data analysis and storage technologies …”

Section: Introductionmentioning

confidence: 99%

“…The proposed solution makes use of a new open source Platform‐as‐a‐Service (PaaS) called Tengu . The Tengu platform provides researchers a time‐saving approach for building big data analysis frameworks through automated installation, configuration, and integration of big data analysis and storage technologies …”

Section: Introductionmentioning

confidence: 99%

Data transformation as a means towards dynamic data storage and polyglot persistence

et al. 2017

Self Cite

View full text Add to dashboard Cite

SummaryLegacy applications have been built around the concept of storing their data in one relational data store. However, with the current differentiation in data store technologies as a consequence of the NoSQL paradigm, new and possibly more performant storage solutions are available to all applications. The concept of dynamic storage makes sure that application data are always stored in the most optimal data store at a given time to increase application performance. Additionally, polyglot persistence aims to push this performance even further by storing each different data type of an application in the data store technology best suited for it. To get legacy applications into dynamic storage and polyglot persistence, schema and data transformations between data store technologies are needed. This usually infers application redesigns as well to support the new data stores. This paper proposes such a transformation approach through a canonical model. It is based on the Lambda architecture to ensure no application downtime is needed during the transformation process, and after the transformation, the application can continue to query in the original query language, thus requiring no application code changes.

show abstract

Tengu: An Experimentation Platform for Big Data Applications

Cited by 10 publications

References 6 publications

Interactive querying and data visualization for abuse detection in social network sites

Interactive querying and data visualization for abuse detection in social network sites

City of Things: Enabling Resource Provisioning in Smart Cities

Data transformation as a means towards dynamic data storage and polyglot persistence

Contact Info

Product

Resources

About