NDLI: TOPiCo: detecting most frequent items from multiple high-rate event streams

Please wait, while we are loading the content...

Model-based verification of event-driven business processes

Herding "small" streaming queries

Distributed control plane for software-defined networks: a case study using event-based middleware

An algebra for pattern matching, time-aware aggregates and partitions on relational data streams

The uncertain case of credit card fraud detection

When artificial intelligence meets the internet of things

The DEBS 2015 grand challenge

Operator fission for load balancing in distributed heterogeneous data stream processing systems

Herakles: real-time sport analysis using a distributed data stream management system

Efficient analysis of event processing applications

TOPiCo: detecting most frequent items from multiple high-rate event streams

Dissemination of anonymized streaming data

An adaptive replication scheme for elastic data stream processing systems

Autofunk, a fast and scalable framework for building formal models from production systems

Solution patterns for realtime streaming analytics

Real time data analysis of taxi rides using StreamMine3G

A complex event processing for large-scale M2M services and its performance evaluations

Approximative event processing on sensor data streams

Complex event processing for reactive security monitoring in virtualized computer systems

Quality-driven processing of sliding window aggregates over out-of-order data streams

Towards prioritized event matching in a content-based publish/subscribe system

ThingStore: a platform for internet-of-things application development and deployment

Dynamic monitoring for improving worker safety at the workplace: use case from a manufacturing shop floor

Tackling variety in event-based systems

Continuous analytics on geospatial data streams with WSO2 complex event processor

Towards content-based publish/subscribe for distributed social networks

Spontaneous and ephemeral social networks: an event-based framework

Lazy evaluation methods for detecting complex events

Efficient key grouping for near-optimal load balancing in stream processing systems

Analyzing content-based publish/subscribe systems

Improving packet processing performance in the ATLAS FELIX project: analysis and optimization of a memory-bounded algorithm

Spring XD: a modular distributed stream and batch processing system

RDF stream processing with CQELS framework for real-time analysis

Distributed QoS-aware scheduling in storm

The pipeflow approach: write once, run in different stream-processing engines

Reliable event messaging in big data enterprises: looking for the balance between producers and consumers

Top-K queries in RDF graph-based stream processing with actors

Concept of multiscoping for distributed event-based systems

Reactive stream processing for data-centric publish/subscribe

A high-throughput, scalable solution for calculating frequent routes and profitability of New York taxis

A conceptual model for event-sourced graph computing

A high throughput processing engine for taxi-generated data streams

Deterministic real-time analytics of geospatial data streams through ScaleGate objects

Pimp my taxi ride

Using odysseus for real-time analysis over high volume geospatial data streams

High performance stream queries in scala

Geospatial event analytics leveraging reactive programming

The PipeFlow approach

Parallel event processing on unbound streams with multi-step windowing

StreamPipes: solving the challenge with semantic stream processing pipelines

TOPiCo: detecting most frequent items from multiple high-rate event streams

Content Provider	ACM Digital Library
Author	Rivière, Etienne Felber, Pascal Oliveira, Rui Schiavoni, Valerio Sutra, Pierre Matos, Miguel
Abstract	Systems such as social networks, search engines or trading platforms operate geographically distant sites that continuously generate streams of events at high-rate. Such events can be access logs to web servers, feeds of messages from participants of a social network, or financial data, among others. The ability to timely detect trends and popularity variations is of paramount importance in such systems. In particular, determining what are the most popular events across all sites allows to capture the most relevant information in near real-time and quickly adapt the system to the load. This paper presents TOPiCo, a protocol that computes the most popular events across geo-distributed sites in a low cost, bandwidth-efficient and timely manner. TOPiCo starts by building the set of most popular events locally at each site. Then, it disseminates only events that have a chance to be among the most popular ones across all sites, significantly reducing the required bandwidth. We give a correctness proof of our algorithm and evaluate TOPiCo using a real-world trace of more than 240 million events spread across 32 sites. Our empirical results shows that (i) TOPiCo is timely and cost-efficient for detecting popular events in a large-scale setting, (ii) it adapts dynamically to the distribution of the events, and (iii) our protocol is particularly efficient for skewed distributions.
Starting Page	58
Ending Page	67
Page Count	10
File Format	PDF
ISBN	9781450332866
DOI	10.1145/2675743.2771838
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Top-k frequency Distributed systems Top-k query Event processing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

MTopS: scalable processing of continuous top-k multi-query workloads

A New Method to Find Top K Items in Data Streams at Arbitrary Time Granularities

Fast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams

Adding structure to top-k: from items to expansions

Complex pattern ranking (CPR): evaluating top-k pattern queries over event streams

Optimal top-K queries processing: Sampling and dynamic scheduling approach

The gist of everything new: personalized top-k processing over web 2.0 streams

Mining top-k frequent items in a data stream with flexible sliding windows

Optimizing Multi-Top-k Queries over Uncertain Data Streams

TOPiCo: detecting most frequent items from multiple high-rate event streams

Similar Documents

MTopS: scalable processing of continuous top-k multi-query workloads

A New Method to Find Top K Items in Data Streams at Arbitrary Time Granularities

Fast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams

Adding structure to top-k: from items to expansions

Complex pattern ranking (CPR): evaluating top-k pattern queries over event streams

Optimal top-K queries processing: Sampling and dynamic scheduling approach

The gist of everything new: personalized top-k processing over web 2.0 streams

Mining top-k frequent items in a data stream with flexible sliding windows

Optimizing Multi-Top-k Queries over Uncertain Data Streams

TOPiCo: detecting most frequent items from multiple high-rate event streams