Timos Sellis

Swinburne University of Technology · Department of Computer Science and Software Engineering

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into...

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, and investors. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we make an important observation as follows – besides the i...

Social networks with location enabling technologies, also known as geo-social networks, allow users to share their location-specific activities and preferences through check-ins. A user in such a geo-social network can be attributed to an associated location (spatial), her preferences as keywords (textual), and the connectivity (social) with her fr...

The appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the diverse demands of regulatory compliance an...

With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to improve the quality of services in some applications such as recommendation systems, advertising, and group format...

Understanding urban areas of interest (AOIs) is essential in many real-life scenarios, and such AOIs can be computed based on the geographic points that satisfy user queries. In this article, we study the problem of efficient and effective visualization of user-defined urban AOIs in an interactive manner. In particular, we first define the problem...

Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to bu...

Influential community search (ICS) on a graph finds a closely connected group of vertices having a dominance over other groups of vertices. The ICS has many applications in recommendations, event organization, and so on. In this paper, we introduce a new variant of ICS, namely keyword-aware influential community query (KICQ), that finds the communi...

RDF data has been extensively deployed describing various types of resources in a structured way. Links between data elements described by RDF models stand for the core of Semantic Web. The rising amount of structured data published in public RDF repositories, also known as Linked Open Data, elucidates the success of the global and unified dataset...

Topic trajectory information provides crucial insight into the dynamics of topics and their evolutionary relationships over a given time. Also, this information can help to improve our understanding on how new topics have emerged or formed through a sequential or interrelated events of emergence, modification and integration of prior topics. Nevert...

In digital repositories, it is crucial to refine existing subject terms and exploit a taxonomy with subject terms, in order to promote information retrieval tasks such as indexing, cataloging and searching of digital documents. In this paper, we address how to refine an existing set of subject terms, often containing irrelevant ones or creating noi...

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categor...

The advent of healthcare information management systems (HIMSs) continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics (BDA) in healthcare can, for instance, he...

Universities collect an enormous amount of student data that remains underutilized in helping decisions or policymaking. Strategies to deal with problems such as drop-out students and low achievers are imperative to the success of universities. Our work is motivated by the fact that student journey in a learning environment, such as a university, r...

A large variety of Computer Science (CS) and Information and Communications Technology (ICT) programs are offered in different institutions across Australia. Even the same institution has several majors of CS programs due to its recent popularity among students and demand in the job market. Current practices in CS education lack a unified approach...

Learning users’ preferences is critical to personalized search and recommendation. Most such systems depend on lists of items rank-ordered according to the user’s preference. Ideally, we want the system to adjust its estimate of users’ preferences after every interaction, thereby becoming progressively better at giving the user what she wants. We a...

Computer Science (CS) education is increasingly becoming popular across the globe. With the increased popularity of CS and Information and Communications Technology (ICT) courses, the ability to measure the similarity/distance between units can aid the decision-making process of education providers during learning path recommendations, evaluating u...

Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to bu...

With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to build their profiles to improve the quality of services in some applications such as recommendation systems, adver...

With a rapid growth in the global population, the modern world is undergoing a rapid expansion of residential areas, especially in urban centres. This continuously demands for increased general services and basic amenities, which are required according to the kind of population associated with the places. The advent of location-based online social...

Conventional DNN training paradigms typically rely on one training set and one validation set, obtained by partitioning an annotated dataset used for training, namely gross training set, in a certain way. The training set is used for training the model while the validation set is used to estimate the generalization performance of the trained model...

Recommender systems based on collaborative filtering suggest items to users according to the similarity of items or similarity of preferences of other users. Latent factor models produce rather accurate predictions of user preferences, but the latency of the features extracted make it difficult to substantiate a recommendation to a user. The realis...

Influence maximization has recently received significant attention for scheduling online campaigns or advertisements on social network platforms. However, most studies only focus on user influence via cyber interactions while ignoring their physical interactions which are also essential to gauge influence propagation. Additionally, targeted campaig...

Measures of centrality of vertices in a network are usually defined solely on the basis of the network structure. In highly dynamic networks, where vertices appear and disappear and their connectivity constantly changes, we need to redefine our measures of centrality to properly capture the temporal dimension of the network structure evolution, as...

To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is...

We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching with the query terms (words o...

This paper focuses on facilitating state-of-the-art applications of big data analytics ( BDA ) architectures and infrastructures to telecommunications ( telecom ) industrial sector. Telecom companies are dealing with terabytes to petabytes of data on a daily basis. IoT applications in telecom are further contributing to this data deluge. Recent adv...

The widespread availability of GPS and the growing popularity of location based social networking applications such as Flickr, Yelp, etc., enable more and more users to share their route activities or trajectories. At the same time, the recent advancement in large-scale 3D modeling has inspired applications that combine visibility and spatial queri...

Location details of social users are important in diverse applications ranging from news recommendation systems to disaster management. However, user location is not easy to obtain from social networks because many users do not bother to provide this information or decline to do so due to privacy concerns. Thus, it is useful to estimate user locati...

In this paper, we study the problem of large-scale trajectory data clustering, k-paths, which aims to efficiently identify k "representative" paths in a road network. Unlike traditional clustering approaches that require multiple data-dependent hyperparameters, k-paths can be used for visual exploration in applications such as traffic monitoring, p...

Graphs, such as social, road and information networks, are ubiquitous as they naturally model entities and their relationships. Many query processing tasks on graphs are concerned about efficiently accessing nodes and edges stored in some order on disk or main memory. A natural following question we focus on here is: given a directed graph, how sho...

Understanding urban areas of interest (AOIs) is essential to decision making in various urban planning and exploration tasks. Such AOIs can be computed based on the geographic points that satisfy the user query. In this demo, we present an interactive visualization system of urban AOIs, supported by a parameter-free and efficient footprint method c...

Open multidimensional data from existing sources and social media often carries insightful information on social issues. With the increase of high volume data and the proliferation of visual analytics platforms, users can more easily interact with and pick out meaningful information from a large dataset. In this paper, we present VisCrime, a system...

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profi...

The Internet of Things (IoT) is springboarding novel applications and has led to the generation of massive amounts of data that can offer valuable insights across multiple domains: Smart Cities, environmental monitoring, healthcare etc. In particular, the availability of open IoT data streaming from heterogeneous sources constitute a novel powerful...

Emerging availability (and varying complexity and types) of Internet of Things (IoT) devices, along with large data volumes that such devices (can potentially) generate, can have a significant impact on our lives, fuelling the development of critical next-generation services and applications in a variety of application domains (e.g. healthcare, sma...

A socio spatial group query finds a group of users who possess strong social connections with each other and have the minimum aggregate spatial distance to a meeting point. Existing studies limit to either finding the best group of a fixed size for a single meeting location, or a single group of a fixed size w.r.t. multiple locations. However, it i...

Sweep coverage is an important covering technique in mobile crowdsensing, in which users or participants are employed to periodically monitor a set of points of interest (POIs) each with a weight indicating the value of its information to be collected. Traditionally, each user proposes a route along which there is a set of POIs to be monitored. The...

The problem of optimal location selection based on reverse k nearest neighbor (R \(k\) NN) queries has been extensively studied in spatial databases. In this work, we present a related query, denoted as a Maximized Bichromatic Reverse Spatial Textual k Nearest Neighbor (MaxST) query, that finds an optimal location and a set of keywords for an objec...

Many modern applications and systems represent and exchange data in tree-structured form and process and produce large tree datasets. Discovering informative patterns in large tree datasets is an important research area that has many practical applications. Along the years, research has evolved from mining induced patterns to mining embedded patter...

Ensemble learning is a powerful machine learning paradigm which leverages a collection of diverse base learners to achieve better prediction performance than that could be achieved by any individual base learner. This work proposes an evolutionary feature subspaces generation based ensemble learning framework, which formulates the tasks of searchin...

The classical disjoint shortest path problem has recently recalled interests from researchers in the network planning and optimization community. However, the requirement of the shortest paths being completely vertex or edge disjoint might be too restrictive and demands much more resources in a network. Partially disjoint shortest paths, in which a...

In this paper we study the problem of supporting effective and scalable visualization for the rapidly increasing volumes of urban data. From an extensive literature study, we find that the existing solutions suffer from at least one of the drawbacks below: (i) loss of interesting structures/outliers due to sampling; (ii) supporting heatmaps only, w...

In this paper we study the problem of supporting effective and scalable visualization for the rapidly increasing volumes of urban data. From an extensive literature study, we find that the existing solutions suffer from at least one of the drawbacks below: (i) loss of interesting structures/outliers due to sampling; (ii) supporting heatmaps only, w...

In this paper, we investigate the problem of clustering distributed multidimensional data streams. We devise a distributed clustering framework DistClusTree that extends the centralized ClusTree approach. The main difficulty in distributed clustering is balancing communication cost and clustering quality. We tackle this in DistClusTree through comb...

Graph database systems are increasingly being used to store and query large-scale property graphs with complex relationships. Graph data, particularly the ones generated from social networks generally has text associated to the graph. Although graph systems provide support for efficient graph-based queries, there have not been comprehensive studies...

Since the mid-2000s, everal indexing techniques have been proposed to efficiently answer top-k spatial-textual queries. However, all of these approaches focus on answering one query at a time. In contrast, how to design efficient algorithms that can exploit similarities between incoming queries to improve performance has received little attention....

Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their...

With the widespread use of GPS-enabled mobile devices, an unprecedented amount of trajectory data is becoming available from various sources such as Bikely, GPS-wayPoints, and Uber. The rise of innovative transportation services and recent break-throughs in autonomous vehicles will lead to the continued growth of trajectory data and related applica...

Urban data (e.g., real estate data, crime data) often have multiple attributes which are highly geography-related. With the scale of data increases, directly visualizing millions of individual data points on top of a map would overwhelm users' perceptual and cognitive capacity and lead to high latency when users interact with the data. In this demo...

In this paper, we present HomeSeeker, an interactive visual analytics system to serve users with different backgrounds of the local real estate market and meet different degrees of user requirements. As a result, HomeSeeker augments existing commercial systems to help users discover hidden patterns, link various location-centered data to the price,...

The urban road networks undergo frequent traffic congestions during the peak hours and around the city center. Capturing the spatiotemporal evolution of the congestion scenario in real-time in an urban-scale can aid in developing smart traffic management systems, and guiding commuters in making informed decision about route choice. The congestion s...

Given a directed graph, how should we label both its outgoing and incoming edges to achieve better disk locality and support neighborhood-related edge queries? In this paper, we answer this question with edge-labeling schemes GrdRandom and FlipInOut, to label edges with integers based on the premise that edges should be assigned integer identifiers...

The ever increasing size of graphs makes them difficult to query and store. In this paper, we present Shrink, a compression method that reduces the size of the graph while preserving the distances between the nodes. The compression is based on the iterative merging of the nodes. During each merging, a system of linear equations is solved to define...

This paper presents a method to automatically estimate parameters for density-based clustering based on data distribution. It also includes several techniques for visualizing the clusters over a map, useful for interactive data exploration. The proposed method enables parameter estimation to automatically adapt to multiple resolutions, allowing the...

GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. In this paper, we study a new kind of query-Reve...

Individuals use Twitter for personal communication, whereas businesses, politicians and celebrities use Twitter for branding purposes. Distinguishing Personal from Branding Twitter accounts is important for Twitter analytics. Existing studies of Twitter account classification apply classical supervised learning, which requires intensive manual anno...