DISC: Density-Based Incremental Clustering by Striding over Streaming Data

2021 ◽  
Author(s):  
Bogyeong Kim ◽  
Kyoseung Koo ◽  
Juhun Kim ◽  
Bongki Moon
Keyword(s):  
Streaming Data ◽  
Incremental Clustering

scholarly journals TwitterNews: Real time event detection from the Twitter data stream

2016 ◽  
Author(s):  
Mahmud Hasan ◽  
Mehmet A Orgun ◽  
Rolf Schwitter
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Detection System ◽  
Fixed Time ◽  
Streaming Data ◽  
Locality Sensitive Hashing ◽  
Vector Model ◽  
Incremental Clustering ◽  
Random Indexing ◽  
Novel Approach

Research in event detection from the Twitter streaming data has been gaining momentum in the last couple of years. Although such data is noisy and often contains misleading information, Twitter can be a rich source of information if harnessed properly. In this paper, we propose a scalable event detection system, TwitterNews, to detect and track newsworthy events in real time from Twitter. TwitterNews provides a novel approach, by combining random indexing based term vector model with locality sensitive hashing, that aids in performing incremental clustering of tweets related to various events within a fixed time. TwitterNews also incorporates an effective strategy to deal with the cluster fragmentation issue prevalent in incremental clustering. The set of candidate events generated by TwitterNews are then filtered, to report the newsworthy events along with an automatically selected representative tweet from each event cluster. Finally, we evaluate the effectiveness of TwitterNews, in terms of the recall and the precision, using a publicly available corpus.

scholarly journals TwitterNews: Real time event detection from the Twitter data stream

2016 ◽  
Author(s):  
Mahmud Hasan ◽  
Mehmet A Orgun ◽  
Rolf Schwitter
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Detection System ◽  
Fixed Time ◽  
Streaming Data ◽  
Locality Sensitive Hashing ◽  
Vector Model ◽  
Incremental Clustering ◽  
Random Indexing ◽  
Novel Approach

Research in event detection from the Twitter streaming data has been gaining momentum in the last couple of years. Although such data is noisy and often contains misleading information, Twitter can be a rich source of information if harnessed properly. In this paper, we propose a scalable event detection system, TwitterNews, to detect and track newsworthy events in real time from Twitter. TwitterNews provides a novel approach, by combining random indexing based term vector model with locality sensitive hashing, that aids in performing incremental clustering of tweets related to various events within a fixed time. TwitterNews also incorporates an effective strategy to deal with the cluster fragmentation issue prevalent in incremental clustering. The set of candidate events generated by TwitterNews are then filtered, to report the newsworthy events along with an automatically selected representative tweet from each event cluster. Finally, we evaluate the effectiveness of TwitterNews, in terms of the recall and the precision, using a publicly available corpus.

Ultra-Wide-Band Wireless Sensor Network for Transmission of Streaming Data

2018 ◽  
Vol 7 (1) ◽  
pp. 91-102
Author(s):  
Yu.V. Andreyev ◽  
◽  
L.V. Kuzmin ◽  
M.G. Popov ◽  
A.I. Ryshov ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wide Band ◽  
Streaming Data ◽  
Wireless Sensor ◽  
Ultra Wide Band

Opinion Mining with Real Time Ontology Streaming Data

2019 ◽  
Vol 23 (1) ◽  
pp. 346-357
Author(s):  
Vithya G ◽  
Naren J ◽  
Varun V
Keyword(s):  
Real Time ◽  
Opinion Mining ◽  
Streaming Data

LASH Tree: LASSO Regression Hoeffding for Streaming Data

2020 ◽  
Vol 24 (04) ◽  
pp. 3022-3033
Author(s):  
Christy Sujatha D ◽  
Gnana Jayanthi Dr.J
Keyword(s):  
Streaming Data ◽  
Lasso Regression

scholarly journals EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

2019 ◽  
Vol 9 (12) ◽  
pp. 2560 ◽  
Author(s):  
Yunkon Kim ◽  
Eui-Nam Huh
Keyword(s):  
Intelligent Transportation System ◽  
Streaming Data ◽  
Data Traffic ◽  
High Quality ◽  
Data Caching ◽  
Resource Limited ◽  
Quality Video ◽  
Key Factor ◽  
Efficient Data ◽  
Art Research

This paper explores data caching as a key factor of edge computing. State-of-the-art research of data caching on edge nodes mainly considers reactive and proactive caching, and machine learning based caching, which could be a heavy task for edge nodes. However, edge nodes usually have relatively lower computing resources than cloud datacenters as those are geo-distributed from the administrator. Therefore, a caching algorithm should be lightweight for saving computing resources on edge nodes. In addition, the data caching should be agile because it has to support high-quality services on edge nodes. Accordingly, this paper proposes a lightweight, agile caching algorithm, EDCrammer (Efficient Data Crammer), which performs agile operations to control caching rate for streaming data by using the enhanced PID (Proportional-Integral-Differential) controller. Experimental results using this lightweight, agile caching algorithm show its significant value in each scenario. In four common scenarios, the desired cache utilization was reached in 1.1 s on average and then maintained within a 4–7% deviation. The cache hit ratio is about 96%, and the optimal cache capacity is around 1.5 MB. Thus, EDCrammer can help distribute the streaming data traffic to the edge nodes, mitigate the uplink load on the central cloud, and ultimately provide users with high-quality video services. We also hope that EDCrammer can improve overall service quality in 5G environment, Augmented Reality/Virtual Reality (AR/VR), Intelligent Transportation System (ITS), Internet of Things (IoT), etc.

scholarly journals Aggregating Everyday Outfits by Incremental Clustering with Interactive User Adaptation

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yasutomo Kawanishi ◽  
Hiroshi Murase ◽  
Satoshi Komorita ◽  
Sei Naito
Keyword(s):  
Incremental Clustering ◽  
User Adaptation ◽  
Interactive User

scholarly journals Modeling advanced security aspects of key exchange and secure channel protocols

2020 ◽  
Vol 62 (5-6) ◽  
pp. 287-293
Author(s):  
Felix Günther
Keyword(s):  
Building Blocks ◽  
Key Exchange ◽  
Internet Security ◽  
Streaming Data ◽  
Transport Layer ◽  
Security Model ◽  
Secret Key ◽  
Multi Stage ◽  
Key Exchange Protocols ◽  
Secure Channel

AbstractSecure connections are at the heart of today’s Internet infrastructure, protecting the confidentiality, authenticity, and integrity of communication. Achieving these security goals is the responsibility of cryptographic schemes, more specifically two main building blocks of secure connections. First, a key exchange protocol is run to establish a shared secret key between two parties over a, potentially, insecure connection. Then, a secure channel protocol uses that shared key to securely transport the actual data to be exchanged. While security notions for classical designs of these components are well-established, recently developed and standardized major Internet security protocols like Google’s QUIC protocol and the Transport Layer Security (TLS) protocol version 1.3 introduce novel features for which supporting security theory is lacking.In my dissertation [20], which this article summarizes, I studied these novel and advanced design aspects, introducing enhanced security models and analyzing the security of deployed protocols. For key exchange protocols, my thesis introduces a new model for multi-stage key exchange to capture that recent designs for secure connections establish several cryptographic keys for various purposes and with differing levels of security. It further introduces a formalism for key confirmation, reflecting a long-established practical design criteria which however was lacking a comprehensive formal treatment so far. For secure channels, my thesis captures the cryptographic subtleties of streaming data transmission through a revised security model and approaches novel concepts to frequently update key material for enhanced security through a multi-key channel notion. These models are then applied to study (and confirm) the security of the QUIC and TLS 1.3 protocol designs.

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