Event detection from social network streams using frequent pattern mining with dynamic support values

Detecting events from streams of data is challenging due to the characteristics of such streams: data elements arrive in real-time and at high velocity, and the size of the streams is typically unbounded while it is not possible to backtrack over past data elements or maintain and review the entire history. Social networks are a good source for event identification as they generate huge amount of timely information representing what users are posting and discussing. In this research, we are developing methods for event detection from streams of data. More specifically, we are presenting a framework for detecting the daily occurring events or topics occurring in social network streams related to major events. Our approach utilizes the Frequent Pattern Mining method to detect the daily occurring frequent patterns, which are going to be our detected events. In addition, we propose a dynamic support definition method to replace the fixed given one. An experiment was run on two streams relating to two different major events to examine the detected events and to test our support definition method. The UK General Elections 2015 stream holds more than one million tweets, and the Greece Crisis 2015 stream contains more than 150k tweets. The detected events were evaluated against news headlines published the same day the event was found. The results revealed that the higher the streaming level (bigger window size), the more accurate the detected events. We also show that for too small sized windows, a more strict support definition method is needed to avoid detecting false or insignificant events

In-Network Outlier Detection in Wireless Sensor Networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on Distributed Computing Systems 200

Developing a novel approach to analyse the regimes of temporary streams and their controls on aquatic biota

Temporary streams are those water courses that undergo the recurrent cessation of flow or the complete drying of their channel. The biological communities in temporary stream reaches are strongly dependent on the temporal changes of the aquatic habitats determined by the hydrological conditions. The use of the aquatic fauna structural and functional characteristics to assess the ecological quality of a temporary stream reach can not therefore be made without taking into account the controls imposed by the hydrological regime. This paper develops some methods for analysing temporary streams' aquatic regimes, based on the definition of six aquatic states that summarize the sets of mesohabitats occurring on a given reach at a particular moment, depending on the hydrological conditions: flood, riffles, connected, pools, dry and arid. We used the water discharge records from gauging stations or simulations using rainfall-runoff models to infer the temporal patterns of occurrence of these states using the developed aquatic states frequency graph. The visual analysis of this graph is complemented by the development of two metrics based on the permanence of flow and the seasonal predictability of zero flow periods. Finally, a classification of the aquatic regimes of temporary streams in terms of their influence over the development of aquatic life is put forward, defining Permanent, Temporary-pools, Temporary-dry and Episodic regime types. All these methods were tested with data from eight temporary streams around the Mediterranean from MIRAGE project and its application was a precondition to assess the ecological quality of these streams using the current methods prescribed in the European Water Framework Directive for macroinvertebrate communities

Computation on abstract data types. The extensional approach, with an application to streams

AbstractIn this paper we specialize the notion of abstract computational procedure previously introduced for intensionally presented structures to those which are extensionally given. This is provided by a form of generalized recursion theory which uses schemata for explicit definition, conditional definition and least fixed point (LFP) recursion in functional of type level ⩽ 2 over any appropriate structure. It is applied here to the case of potentially infinite (and more general partial) streams as an abstract data type

Clustering Memes in Social Media

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'13), 201

Influence of Structural Disturbance on Stream Function and Macroinvertebrate Communities in Upper Coastal Plain Headwater Streams

Freshwater is a resource under threat due to anthropogenic actions. Stream restoration is a common method for mitigating disturbance. Inconsistent methodologies used for evaluating restorations have drawn criticism. Limited use of baseline data for guiding stream restoration activities is of particular concern. This study was developed to elucidate metrics that differentiate reference and disturbed sites in Upper Coastal Plain streams. This information could improve resource use and successes of restorations. Structural and functional variables were examined in 10 reference and 10 streams that meet the traditional definition of disturbance and would be restoration priorities. Disturbed streams were classified into two regimes, temporal, based on time since disturbance, and categorical, based on disturbance cause. Some metrics of geomorphology, water chemistry and macroinvertebrates differentiated reference from disturbed regimes and while other metrics separated streams within disturbance regimes. Surprisingly, leaf decay rate was not an effective metric for determining disturbance. However, macroinvertebrate leaf pack colonizers were found to be useful for differentiating reference sites and disturbance regimes. Of the 10 disturbed streams this study examined, my data suggests that only three are in immediate need of restoration. This study emphasizes the importance of baseline data and its potential benefits for guiding stream restoration