Our Research

Our research focuses on (but is not limited to) the following areas:
  • Web Mining

    "Web mining - is the application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are Web usage mining, Web content mining and Web structure mining.", as Wikipedia mentions.

    Over the last few years, blogs, microblogs and other platforms that can host user-generated content have gained massive popularity. The huge growth of user generated content in the web provides a wealth of information waiting to be extracted. Blog analysis and searching in blogs introduces new challenges for research in information retrieval, data mining and knowledge discovery in general.

    • How the Live Web Feels About Events. G. Valkanas, D. Gunopulos, CIKM 2013
    • Rank-Aware Crawling of Hidden-Web Sites. G. Valkanas, A. Ntoulas and D. Gunopulos, WebDB 2011
    • Searching for events in the blogosphere. M.Platakis, D. Kotsakos and D. Gunopulos, Poster paper, WWW 2009
  • Text Mining

    Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).
    • Efficient and Domain-Invariant Competitor Mining. T. Lappas, G. Valkanas, D. Gunopulos, KDD 2012
    • Efficient confident search in large review corpora. T. Lappas, D. Gunopulos, ECML/PKDD 2010
    • On burstiness-aware search for document sequences. T. Lappas, B. Arai, M. Platakis, D. Kotsakos and D. Gunopulos, KDD 2009
  • Social Networks

    A social network is a social structure made up of individuals (or organizations) called "nodes", which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige.

    Social network analysis views social relationships in terms of network theory consisting of nodes and ties (also called edges, links, or connections). Nodes are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph-based structures are often very complex. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.

    • Finding effectors in social networks. T. Lappas, E. Terzi, D. Gunopulos, H. Mannila, KDD 2010
    • Interactive recommendations in social endorsement networks. T. Lappas and D. Gunopulos, RECSYS 2010
  • Wireless Sensor Networks Data Management

    A recent development, catalysed by advances in radio, processor and battery technology, is the emergence of sensor networks as an economically viable hardware platform with which to observe or monitor phenomena in situ, possibly over a wide area of interest and at a finer grain of observation than was previously possible. Such networks are formed from sensor nodes working collaboratively on a task, which may involve data collection, event detection or entity tracking. Applications in a variety of domains have been proposed for sensor networks, including environmental monitoring, military, security and surveillance, precision agriculture, industrial applications, smart buildings, asset tracking and supply chain management, and health monitoring. The ability of sensor network nodes to gather information about their surrounding, process data, communicate with each other, and perform actuation (i.e. alter their surroundings in some way) has led to the idea that sensor networks conceptually enable the physical world itself to be viewed as a computing platform.

    • Extending Query Languages for In-Network Query Processing, G. Valkanas, D. Gunopulos, I. Galpin, J.G. A. Gray and A.A. A. Fernandes A.A. A., MobiDE 2011
    • Deploying In-Network Data Analysis Techniques in Sensor Networks, Valkanas G., Kotsifakos A., Gunopulos D., Galpin I., Gray J.G. A., Fernandes A.A. A., Paton W. N., MDM 2011
    • A Distributed Technique for Dynamic Operator Placement in Wireless Sensor Networks. G. Chatzimilioudis, N. Mamoulis and D. Gunopulos,. MDM 2010
    • Region Sampling: Continuous Adaptive Sampling on Sensor Networks, S. Lin, B. Arai, D. Gunopulos and G. Das, ICDE 2008
  • Geospatial Systems

    Geospatial is a term widely used to describe the combination of spatial software and analytical methods with terrestrial or geographic datasets. The term is often used in conjunction with geographic information systems and geomatics, never separately.", as Wikipedia mentions.
  • Time Series Analysis

    In statistics, signal processing, econometrics and mathematical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Examples of time series are the daily closing value of the Dow Jones index or the annual flow volume of the Nile River at Aswan. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to forecast future events based on known past events to predict data points before they are measured. An example of time series forecasting in econometrics is predicting the opening price of a stock based on its past performance. Time series are very frequently plotted via line charts.
    • A subsequence matching with gaps-range-tolerances framework: a query-by-Humming application. A. Kotsifakos, P. Papapetrou, J. Hollmén and D. Gunopulos, PVLDB 2011.
    • Embedding-based subsequence matching in time series databases. P. Papapetrou, V. Athitsos, M. Potamias, G. Kollios and D. Gunopulos, TODS 2011.
    • Approximate embedding-based subsequence matching of time series. V. Athitsos, P. Papapetrou, M. Potamias, G. Kollios and D. Gunopulos, SIGMOD 2008