Streaming Event Detection in Microblogs: Balancing Accuracy and Performance

Karagöz, Pınar
In this work, we model the problem of online event detection in microblogs as a stateful stream processing problem and offer a novel solution that balances result accuracy and performance. Our new approach builds on two state of the art algorithms. The first algorithm is based on identifying bursty keywords inside blocks of blog messages. The second one involves clustering blog messages based on similarity of their contents. To combine the computational simplicity of the keyword-based algorithm with the semantic accuracy of the clustering-based algorithm, we propose a new hybrid algorithm. We then implement these algorithms in a streaming manner, on top of Apache Storm augmented with Apache Cassandra for state management. Experiments with a 12M tweet dataset from Twitter show that our hybrid approach provides a better accuracy-performance compromise than the previous approaches.


Event Detection by Change Tracking on Community Structure of Temporal Networks
Aktunc, Riza; Toroslu, İsmail Hakkı; Karagöz, Pınar (2018-08-31)
Event detection is a popular research problem, aiming to detect events from online data sources with least possible delay. Most of the previous work focus on analyzing textual content such as social media postings to detect happenings. In this work, we consider event detection as a change detection problem in network structure, and propose a method that detects change in community structure extracted from communication network. We study three versions of the method based on different change models. Experime...
Ozkan, Huseyin; Ozkan, Fatih; Delibalta, Ibrahim; KOZAT, SÜLEYMAN SERDAR (2015-09-20)
We propose a computationally highly scalable online anomaly detection algorithm for time series, which achieves - with no parameter tuning- a specified false alarm rate while minimizing the miss rate. The proposed algorithm sequentially operates on a fast streaming temporal data, extracts the nominal attributes under possibly varying Markov statistics and then declares an anomaly when the observations are statistically sufficiently deviant. Regardless of whether the source is stationary or non-stationary, o...
Multi-objective decision making using fuzzy discrete event systems: A mobile robot example
Boutalis, Yiannis; Schmidt, Klaus Verner (2010-09-29)
In this paper, we propose an approach for the multi-objective control of sampled data systems that can be modeled as fuzzy discrete event systems (FDES). In our work, the choice of a fuzzy system representation is justified by the assumption of a controller realization that depends on various potentially imprecise sensor measurements. Our approach consists of three basic steps that are performed in each sampling instant. First, the current fuzzy state of the system is determined by a sensor evaluation. Seco...
Event detection on social media using transaction based stream processing engine
Çınar, Hüseyin Alper; Karagöz, Pınar; Department of Computer Engineering (2019)
The aim of this study is detecting events on social media by improving current solutions in terms of accuracy and time performance. An event is something that occurs in a short duration of time in a certain place. In this thesis, the problem is modelled as a streaming transaction process. Three different event detection method is adapted to our solution. First one is the keyword-based event detection method that looks for bursty keywords in a period. The second one is the clustering-based event detection me...
Radar target detection in non-gaussian clutter
Doyuran, Ülkü; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2007)
In this study, novel methods for high-resolution radar target detection in non-Gaussian clutter environment are proposed. In solution of the problem, two approaches are used: Non-coherent detection that operates on the envelope-detected signal for thresholding and coherent detection that performs clutter suppression, Doppler processing and thresholding at the same time. The proposed non-coherent detectors, which are designed to operate in non-Gaussian and range-heterogeneous clutter, yield higher performanc...
Citation Formats
O. C. SAHIN, P. Karagöz, and N. TATBUL, “Streaming Event Detection in Microblogs: Balancing Accuracy and Performance,” 2019, Accessed: 00, 2020. [Online]. Available: