From the recent proliferation of social media channels to the immense amount of user-generated co... more From the recent proliferation of social media channels to the immense amount of user-generated content, an increasing interest in social media mining is currently being witnessed. Messages continuously posted via these channels report a broad range of topics from daily life to global and local events. As a consequence, this has opened new opportunities for mining event information crucial in many application domains, especially in increasing the situational awareness in critical scenarios. Interestingly, many of these messages are enriched with location information, due to the widespread of mobile devices and the recent advancements of today's location acquisition techniques. This enables location-aware event mining, i.e., the detection and tracking of localized events. In this thesis, we propose novel frameworks and models that digest social media content for localized event detection, tracking, and recommendation. We first develop KeyPicker, a framework to extract and score event-related keywords in an online fashion, accounting for high levels of noise, temporal heterogeneity and outliers in the data. Then, LocEvent is proposed to incrementally detect and track events using a 4-stage procedure. That is, LocEvent receives the keywords extracted by KeyPicker, identifies local keywords, spatially clusters them, and finally scores the generated clusters. For each detected event, a set of descriptive keywords, a location, and a time interval are estimated at a fine-grained resolution. In addition to the sparsity of geo-tagged messages, people sometimes post about events far away from an event's location. Such spatial problems are handled by novel spatial regularization techniques, namely, graph-and gazetteer-based regularization. To ensure scalability, we utilize a hierarchical spatial index in addition to a multi-stage filtering procedure that gradually suppresses noisy words and considers only event-related ones for complex spatial computations. Undertaking this PhD has been a truly life-changing experience for me. This work would not have been possible without the support that I received from many people. First of all, I would like to express my deepest gratitude to my advisor, Prof. Dr. Michael Gertz, for giving me the opportunity to be one of his PhD students, it is truly an honor. I am really thankful for his guidance, endless support, immense knowledge, and deep insights that helped me at various stages of my research. I remain amazed that despite his busy schedule, he was able to go through the final draft of my thesis and meet me regularly with comments and suggestions on almost every page. I would also like to thank my dissertation committee for the time, efforts, and precious feedback. During my PhD study and in spite of my busy days, I had a memorable time at the Database Systems Research Group, Heidelberg University. I was lucky to have an impressive research environment with great colleagues. Thank you, Dr. Ayser Armiti, you supported my first step to join this wonderful group. Many thanks to
Microblogging services such as Twitter, Facebook, and Four-square have become major sources for i... more Microblogging services such as Twitter, Facebook, and Four-square have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the tem-poral context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the lo-cation of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are contin-uously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similar-ity. To determine the most important events in a (r...
WSEAS Transactions on Information Science and Applications archive, 2011
In this paper, a new sorting algorithm called the Growing-Tree Sorting Algorithm (GTSA) is propos... more In this paper, a new sorting algorithm called the Growing-Tree Sorting Algorithm (GTSA) is proposed to sort a vector of items in a linear time. It implements a structured tree that stores the digits of each input integer and retrieves the integers in the correct order. If d is the maximum number of digits per input number and n is the input size, then it can be shown that the GTSA algorithm requires θ(dn) time to sort both in its best and worst-case scenarios. It can be also shown that the best-case memory complexity of GTSA is θ(d) and that the worst-case memory complexity is θ(dn). To evaluate the performance of GTSA, it was compared with the radix and counting sort algorithms in terms of the memory they consume and their corresponding execution times. The experiments showed that GTSA was, in general, more memory-conservative than counting sort and radix sorting algorithms. The experiments also showed that the GTSA was faster than the radix and counting sort algorithms when the in...
Mobile computing is dominating the technology market and it is expected to continue growing. Mobi... more Mobile computing is dominating the technology market and it is expected to continue growing. Mobile thirdparty applications without any doubt contribute vastly to this growth. However, intrusive apps that tend to ask for plenty of permissions are becoming a common trend that influence the privacy of mobile users. Solutions have been proposed to detect and remove malicious apps from online markets or detect them after being installed. Yet, dealing with intrusive apps requires high user involvement and best judgment and comprehension. There have been a very few works that aim at helping mobile users make calculated decisions to avoid intrusive apps. In this paper, we are proposing and evaluating a new security-centric ranking algorithm built on top of the Elasticsearch engine to assist users evade installing intrusive apps. The algorithm calculates an intrusiveness score for an app based on its requested permissions, received system actions, and on the privacy preferences of users. In...
Abstract—In this paper, an efficient technique is proposed to manage the cache memory. The propos... more Abstract—In this paper, an efficient technique is proposed to manage the cache memory. The proposed technique introduces some modifications on the well-known set associative mapping technique. This modification requires a little alteration in the structure of the ...
The extraction of named entities from unstructured text is a crucial component in numerous Natura... more The extraction of named entities from unstructured text is a crucial component in numerous Natural Language Processing (NLP) applications such as information retrieval, question answering, machine translation, to name but a few. Named-entity Recognition (NER) aims at locating proper nouns from unstructured text and classifying them into a predefined set of types, such as persons, locations, and organizations. There has been extensive research on improving the accuracy of NER in English text. For other languages such as Arabic, extracting Named-entities is quite challenging due to its morphological structure. In this paper, we introduce ArabiaNer, a system employing Conditional Random Field (CRF) learning algorithm with extensive feature engineering steps to effectively extract Arabic named Entities. ArabiaNer produced state-of-the-art results with f1-score of 91.31% when applied on the ANERcrop dataset.
Abstract—In this paper, an efficient technique is proposed to manage the cache memory. The propos... more Abstract—In this paper, an efficient technique is proposed to manage the cache memory. The proposed technique introduces some modifications on the well-known set associative mapping technique. This modification requires a little alteration in the structure of the ...
Proceedings of the 21st Acm Sigspatial International Conference on Advances in Geographic Information Systems, Nov 5, 2013
ABSTRACT Social networking and microblogging services such as Twitter provide a continuous source... more ABSTRACT Social networking and microblogging services such as Twitter provide a continuous source of data from which useful information can be extracted. The detection and characterization of bursty words play an important role in processing such data, as bursty words might hint to events or trending topics of social importance upon which actions can be triggered. While there are several approaches to extract bursty words from the content of messages, there is only little work that deals with the dynamics of continuous streams of messages, in particular messages that are geo-tagged. In this paper, we present a framework to identify bursty words from Twitter text streams and to describe such words in terms of their spatio-temporal characteristics. Using a time-aware word usage baseline, a sliding window approach over incoming tweets is proposed to identify words that satisfy some burstiness threshold. For these words then a time-varying, spatial signature is determined, which primarily relies on geo-tagged tweets. In order to deal with the noise and the sparsity of geo-tagged tweets, we propose a novel graph-based regularization procedure that uses spatial cooccurrences of bursty words and allows for computing sound spatial signatures. We evaluate the functionality of our online processing framework using two real-world Twitter datasets. The results show that our framework can efficiently and reliably extract bursty words and describe their spatio-temporal evolution over time.
ABSTRACT Microblogging services such as Twitter, Facebook, and Foursquare have become major sourc... more ABSTRACT Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the temporal context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the location of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are continuously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similarity. To determine the most important events in a (recent) time frame, we introduce a scoring scheme for events. We demonstrate the functionality of our system, called Even-Tweet, using a stream of tweets from Europe during the 2012 UEFA European Football Championship.
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL'13, 2013
ABSTRACT It is nowadays possible to access a huge and increasing stream of social media records. ... more ABSTRACT It is nowadays possible to access a huge and increasing stream of social media records. Recently, such data has been used to infer about spatio-temporal phenomena by treating the records as proxy observations of the real world. However, since such observations are heavily uncertain and their spatio-temporal distribution is highly heterogeneous, extracting meaningful signals from such data is a challenging task. In this paper, we present a probabilistic model to extract spatio-temporal distributions of phenomena (called spatio-temporal signals) from social media. Our approach models spatio-temporal and semantic knowledge about real-world phenomena embedded in records on the basis of conditional probability distributions in a Bayesian network. Through this, we realize a generic and comprehensive model where knowledge and uncertainties about spatio-temporal phenomena can be described in a modular and extensible fashion. We show that existing models for the extraction of spatio-temporal phenomena distributions from social media are particular instances of our model. We quantitatively evaluate instances of our model by comparing the spatio-temporal distributions of extracted phenomena from a large Twitter data set to their real-world distributions. The results clearly show that our model allows to extract better spatio-temporal signals in terms of quality and robustness.
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL'13, 2013
ABSTRACT Social networking and microblogging services such as Twitter provide a continuous source... more ABSTRACT Social networking and microblogging services such as Twitter provide a continuous source of data from which useful information can be extracted. The detection and characterization of bursty words play an important role in processing such data, as bursty words might hint to events or trending topics of social importance upon which actions can be triggered. While there are several approaches to extract bursty words from the content of messages, there is only little work that deals with the dynamics of continuous streams of messages, in particular messages that are geo-tagged. In this paper, we present a framework to identify bursty words from Twitter text streams and to describe such words in terms of their spatio-temporal characteristics. Using a time-aware word usage baseline, a sliding window approach over incoming tweets is proposed to identify words that satisfy some burstiness threshold. For these words then a time-varying, spatial signature is determined, which primarily relies on geo-tagged tweets. In order to deal with the noise and the sparsity of geo-tagged tweets, we propose a novel graph-based regularization procedure that uses spatial cooccurrences of bursty words and allows for computing sound spatial signatures. We evaluate the functionality of our online processing framework using two real-world Twitter datasets. The results show that our framework can efficiently and reliably extract bursty words and describe their spatio-temporal evolution over time.
ABSTRACT Shared multimedia, microblogs, search engine queries, user comments, and location check-... more ABSTRACT Shared multimedia, microblogs, search engine queries, user comments, and location check-ins, among others, generate an enormous stream of human activity records. Such records consist of information in the form of text, images, or videos, and can often be traced in time and space using associated time/location information. Over the past years such spatio-temporal activity streams have been heavily studied with the aim to extract and explore spatio-temporal phenomena, like events, place descriptions, and geographical topics. Despite the clear intuition and often simple techniques to extract such knowledge, the amount of noise, sparsity, and heterogeneity in the data makes such tasks non-trivial and erroneous. This demonstration offers a visual interface to compare, combine, and evaluate spatio-temporal signal extraction and exploration approaches from large-scale sets of human activity records.
Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for in... more Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the temporal context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the location of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are continuously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similarity. To determine the most important events in a (recent...
From the recent proliferation of social media channels to the immense amount of user-generated co... more From the recent proliferation of social media channels to the immense amount of user-generated content, an increasing interest in social media mining is currently being witnessed. Messages continuously posted via these channels report a broad range of topics from daily life to global and local events. As a consequence, this has opened new opportunities for mining event information crucial in many application domains, especially in increasing the situational awareness in critical scenarios. Interestingly, many of these messages are enriched with location information, due to the widespread of mobile devices and the recent advancements of today's location acquisition techniques. This enables location-aware event mining, i.e., the detection and tracking of localized events. In this thesis, we propose novel frameworks and models that digest social media content for localized event detection, tracking, and recommendation. We first develop KeyPicker, a framework to extract and score event-related keywords in an online fashion, accounting for high levels of noise, temporal heterogeneity and outliers in the data. Then, LocEvent is proposed to incrementally detect and track events using a 4-stage procedure. That is, LocEvent receives the keywords extracted by KeyPicker, identifies local keywords, spatially clusters them, and finally scores the generated clusters. For each detected event, a set of descriptive keywords, a location, and a time interval are estimated at a fine-grained resolution. In addition to the sparsity of geo-tagged messages, people sometimes post about events far away from an event's location. Such spatial problems are handled by novel spatial regularization techniques, namely, graph-and gazetteer-based regularization. To ensure scalability, we utilize a hierarchical spatial index in addition to a multi-stage filtering procedure that gradually suppresses noisy words and considers only event-related ones for complex spatial computations. Undertaking this PhD has been a truly life-changing experience for me. This work would not have been possible without the support that I received from many people. First of all, I would like to express my deepest gratitude to my advisor, Prof. Dr. Michael Gertz, for giving me the opportunity to be one of his PhD students, it is truly an honor. I am really thankful for his guidance, endless support, immense knowledge, and deep insights that helped me at various stages of my research. I remain amazed that despite his busy schedule, he was able to go through the final draft of my thesis and meet me regularly with comments and suggestions on almost every page. I would also like to thank my dissertation committee for the time, efforts, and precious feedback. During my PhD study and in spite of my busy days, I had a memorable time at the Database Systems Research Group, Heidelberg University. I was lucky to have an impressive research environment with great colleagues. Thank you, Dr. Ayser Armiti, you supported my first step to join this wonderful group. Many thanks to
Microblogging services such as Twitter, Facebook, and Four-square have become major sources for i... more Microblogging services such as Twitter, Facebook, and Four-square have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the tem-poral context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the lo-cation of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are contin-uously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similar-ity. To determine the most important events in a (r...
WSEAS Transactions on Information Science and Applications archive, 2011
In this paper, a new sorting algorithm called the Growing-Tree Sorting Algorithm (GTSA) is propos... more In this paper, a new sorting algorithm called the Growing-Tree Sorting Algorithm (GTSA) is proposed to sort a vector of items in a linear time. It implements a structured tree that stores the digits of each input integer and retrieves the integers in the correct order. If d is the maximum number of digits per input number and n is the input size, then it can be shown that the GTSA algorithm requires θ(dn) time to sort both in its best and worst-case scenarios. It can be also shown that the best-case memory complexity of GTSA is θ(d) and that the worst-case memory complexity is θ(dn). To evaluate the performance of GTSA, it was compared with the radix and counting sort algorithms in terms of the memory they consume and their corresponding execution times. The experiments showed that GTSA was, in general, more memory-conservative than counting sort and radix sorting algorithms. The experiments also showed that the GTSA was faster than the radix and counting sort algorithms when the in...
Mobile computing is dominating the technology market and it is expected to continue growing. Mobi... more Mobile computing is dominating the technology market and it is expected to continue growing. Mobile thirdparty applications without any doubt contribute vastly to this growth. However, intrusive apps that tend to ask for plenty of permissions are becoming a common trend that influence the privacy of mobile users. Solutions have been proposed to detect and remove malicious apps from online markets or detect them after being installed. Yet, dealing with intrusive apps requires high user involvement and best judgment and comprehension. There have been a very few works that aim at helping mobile users make calculated decisions to avoid intrusive apps. In this paper, we are proposing and evaluating a new security-centric ranking algorithm built on top of the Elasticsearch engine to assist users evade installing intrusive apps. The algorithm calculates an intrusiveness score for an app based on its requested permissions, received system actions, and on the privacy preferences of users. In...
Abstract—In this paper, an efficient technique is proposed to manage the cache memory. The propos... more Abstract—In this paper, an efficient technique is proposed to manage the cache memory. The proposed technique introduces some modifications on the well-known set associative mapping technique. This modification requires a little alteration in the structure of the ...
The extraction of named entities from unstructured text is a crucial component in numerous Natura... more The extraction of named entities from unstructured text is a crucial component in numerous Natural Language Processing (NLP) applications such as information retrieval, question answering, machine translation, to name but a few. Named-entity Recognition (NER) aims at locating proper nouns from unstructured text and classifying them into a predefined set of types, such as persons, locations, and organizations. There has been extensive research on improving the accuracy of NER in English text. For other languages such as Arabic, extracting Named-entities is quite challenging due to its morphological structure. In this paper, we introduce ArabiaNer, a system employing Conditional Random Field (CRF) learning algorithm with extensive feature engineering steps to effectively extract Arabic named Entities. ArabiaNer produced state-of-the-art results with f1-score of 91.31% when applied on the ANERcrop dataset.
Abstract—In this paper, an efficient technique is proposed to manage the cache memory. The propos... more Abstract—In this paper, an efficient technique is proposed to manage the cache memory. The proposed technique introduces some modifications on the well-known set associative mapping technique. This modification requires a little alteration in the structure of the ...
Proceedings of the 21st Acm Sigspatial International Conference on Advances in Geographic Information Systems, Nov 5, 2013
ABSTRACT Social networking and microblogging services such as Twitter provide a continuous source... more ABSTRACT Social networking and microblogging services such as Twitter provide a continuous source of data from which useful information can be extracted. The detection and characterization of bursty words play an important role in processing such data, as bursty words might hint to events or trending topics of social importance upon which actions can be triggered. While there are several approaches to extract bursty words from the content of messages, there is only little work that deals with the dynamics of continuous streams of messages, in particular messages that are geo-tagged. In this paper, we present a framework to identify bursty words from Twitter text streams and to describe such words in terms of their spatio-temporal characteristics. Using a time-aware word usage baseline, a sliding window approach over incoming tweets is proposed to identify words that satisfy some burstiness threshold. For these words then a time-varying, spatial signature is determined, which primarily relies on geo-tagged tweets. In order to deal with the noise and the sparsity of geo-tagged tweets, we propose a novel graph-based regularization procedure that uses spatial cooccurrences of bursty words and allows for computing sound spatial signatures. We evaluate the functionality of our online processing framework using two real-world Twitter datasets. The results show that our framework can efficiently and reliably extract bursty words and describe their spatio-temporal evolution over time.
ABSTRACT Microblogging services such as Twitter, Facebook, and Foursquare have become major sourc... more ABSTRACT Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the temporal context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the location of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are continuously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similarity. To determine the most important events in a (recent) time frame, we introduce a scoring scheme for events. We demonstrate the functionality of our system, called Even-Tweet, using a stream of tweets from Europe during the 2012 UEFA European Football Championship.
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL'13, 2013
ABSTRACT It is nowadays possible to access a huge and increasing stream of social media records. ... more ABSTRACT It is nowadays possible to access a huge and increasing stream of social media records. Recently, such data has been used to infer about spatio-temporal phenomena by treating the records as proxy observations of the real world. However, since such observations are heavily uncertain and their spatio-temporal distribution is highly heterogeneous, extracting meaningful signals from such data is a challenging task. In this paper, we present a probabilistic model to extract spatio-temporal distributions of phenomena (called spatio-temporal signals) from social media. Our approach models spatio-temporal and semantic knowledge about real-world phenomena embedded in records on the basis of conditional probability distributions in a Bayesian network. Through this, we realize a generic and comprehensive model where knowledge and uncertainties about spatio-temporal phenomena can be described in a modular and extensible fashion. We show that existing models for the extraction of spatio-temporal phenomena distributions from social media are particular instances of our model. We quantitatively evaluate instances of our model by comparing the spatio-temporal distributions of extracted phenomena from a large Twitter data set to their real-world distributions. The results clearly show that our model allows to extract better spatio-temporal signals in terms of quality and robustness.
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL'13, 2013
ABSTRACT Social networking and microblogging services such as Twitter provide a continuous source... more ABSTRACT Social networking and microblogging services such as Twitter provide a continuous source of data from which useful information can be extracted. The detection and characterization of bursty words play an important role in processing such data, as bursty words might hint to events or trending topics of social importance upon which actions can be triggered. While there are several approaches to extract bursty words from the content of messages, there is only little work that deals with the dynamics of continuous streams of messages, in particular messages that are geo-tagged. In this paper, we present a framework to identify bursty words from Twitter text streams and to describe such words in terms of their spatio-temporal characteristics. Using a time-aware word usage baseline, a sliding window approach over incoming tweets is proposed to identify words that satisfy some burstiness threshold. For these words then a time-varying, spatial signature is determined, which primarily relies on geo-tagged tweets. In order to deal with the noise and the sparsity of geo-tagged tweets, we propose a novel graph-based regularization procedure that uses spatial cooccurrences of bursty words and allows for computing sound spatial signatures. We evaluate the functionality of our online processing framework using two real-world Twitter datasets. The results show that our framework can efficiently and reliably extract bursty words and describe their spatio-temporal evolution over time.
ABSTRACT Shared multimedia, microblogs, search engine queries, user comments, and location check-... more ABSTRACT Shared multimedia, microblogs, search engine queries, user comments, and location check-ins, among others, generate an enormous stream of human activity records. Such records consist of information in the form of text, images, or videos, and can often be traced in time and space using associated time/location information. Over the past years such spatio-temporal activity streams have been heavily studied with the aim to extract and explore spatio-temporal phenomena, like events, place descriptions, and geographical topics. Despite the clear intuition and often simple techniques to extract such knowledge, the amount of noise, sparsity, and heterogeneity in the data makes such tasks non-trivial and erroneous. This demonstration offers a visual interface to compare, combine, and evaluate spatio-temporal signal extraction and exploration approaches from large-scale sets of human activity records.
Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for in... more Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the temporal context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the location of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are continuously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similarity. To determine the most important events in a (recent...
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Papers by Hamed Abdelhaq