Nikolaos Panagiotou

Intelligent Urban Data Monitoring for Smart Cities

Urban data management is already an essential element of modern cities. The authorities can build on the variety of automatically generated information and develop intelligent services that improve citizens daily life, save environmental resources or aid in coping with emergencies. From a data mining perspective, urban data introduce a lot of challenges. Data volume, velocity and veracity are some obvious obstacles. However, there are even more issues of equal importance like data quality, resilience, privacy and security. In this paper we describe the development of a set of techniques and frameworks that aim at effective and efficient urban data management in real settings. To do this, we collaborated with the city of Dublin and worked on real problems and data. Our solutions were integrated in a system that was evaluated and is currently utilized by the city.

Detecting Events in Online Social Networks: Definitions, Trends and Challenges

Event detection is a research area that attracted attention during the last years due to the widespread availability of social media data. The problem of event detection has been examined in multiple social media sources like Twitter, Flickr, YouTube and Facebook. The task comprises many challenges including the processing of large volumes of data and high levels of noise. In this article, we present a wide range of event detection algorithms, architectures and evaluation methodologies. In addition, we extensively discuss on available datasets, potential applications and open research issues. The main objective is to provide a compact representation of the recent developments in the field and aid the reader in understanding the main challenges tackled so far as well as identifying interesting future research directions.

REMI: A framework of reusable elements for mining heterogeneous data with missing information: A Tale of Congestion in Two Smart Cities

Applications targeting smart cities tackle common challenges, however solutions are seldom portable from one city to another due to the heterogeneity of smart city ecosystems. A major obstacle involves the differences in the levels of available information. In this work, we present REMI, which is a mining framework that handles varying degrees of information availability by providing a meta-solution to missing data. The framework core concept is the REMI layered stack architecture, offering two complementary approaches to dealing with missing information, namely data enrichment (DARE) and graceful degradation (GRADE). DARE aims at inference of missing information levels, while GRADE attempts to mine the patterns using only the existing data.We show that REMI provides multiple ways for re-usability, while being fault tolerant and enabling incremental development. One may apply the architecture to different problem instantiations within the same domain, or deploy it across various domains. Furthermore, we introduce the other three components of the REMI framework backing the layered stack. To support decision making in this framework, we show a mapping of REMI into an optimization problem (OTP) that balances the trade-off between three costs: inaccuracies in inference of missing data (DARE), errors when using less information (GRADE), and gathering of additional data. Further, we provide an experimental evaluation of REMI using real-world transportation data coming from two European smart cities, namely Dublin and Warsaw.

REMI, Reusable Elements for Multi-Level Information Availability: Demo

Applications targeting Smart Cities tackle common challenges, however solutions are seldom portable from one city to another due to the heterogeneity of city ecosystems. A major obstacle involves the differences in the levels of available information. In this demonstration we present REMI, a reusable elements framework to handle varying degrees of information availability by design from two complementary angles, namely graceful degradation (GRADE) and data enrichment (DARE). In a nutshell, we develop reusable machine learning black boxes for mining and aggregating streaming data, either to infer missing data from available data, or to adapt expected accuracy based on data availability. We illustrate the proposed approach using tram data from the city of Warsaw.

INSIGHT: Dynamic Traffic Management Using Heterogeneous Urban Data

In this demo we present INSIGHT, a system that provides traffic event detection in Dublin by exploiting Big Data and Crowdsourcing techniques. Our system is able to process and analyze input from multiple heterogeneous urban data sources.

Mining hidden constrained streams in practice: informed search in dynamic filter spaces

In this paper we tackle the recently proposed problem of hidden streams. In many situations, the data stream that we are interested in, is not directly accessible. Instead, part of the data can be accessed only through applying filters (e.g. keyword filtering). In fact this is the case of the most discussed social stream today, Twitter. The problem in this case is how to retrieve as many relevant documents as possible by applying the most appropriate set of filters to the original stream and, at the same time, respect a number of constrains (e.g. maximum number of filters that can be applied). In this work we introduce a search approach on a dynamic filter space. We utilize heterogeneous filters (not only keywords) making no assumptions about the attributes of the individual filters. We advance current research by considering realistically hard constraints based on real-world scenarios that require tracking of multiple dynamic topics. We demonstrate the effectiveness of our approaches on a set of topics of static and dynamic nature. The development of the approach was motivated by a real application. Our system is deployed in Dublin Citys Traffic Management Center and allows the city officers to analyze large sources of heterogeneous data and identify events related to traffic as well as emergencies.