arXiv subjects

Timos Sellis

Publications and source records attributed to Timos Sellis.

18 recordsLinked to original sources

arXiv2024

A Multi-Modal Deep Learning Based Approach for House Price Prediction

Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp

cs.LG↗

arXiv2022

Integrity 2022: Integrity in Social Networks and Media

This is the proposal for the third edition of the Workshop on Integrity in Social Networks and Media, Integrity 2022, following the success of the first two Workshops held in conjunction with the 13th & 14th ACM Conference on Web Search and Data Mining (WSDM) in 2020 and 2021. The goal of the workshop is to bring together researchers and practitioners to discuss content and interaction integrity challenges in social networks and social media platforms. The event consists of (1) a series of invited talks by reputed members of the Integrity community from both academia and industry, (2) a call-for-papers for contributed talks and posters, and (3) a panel with the speakers.

cs.SI↗

arXiv2021

TopicTracker: A Platform for Topic Trajectory Identification and Visualisation

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. Nevertheless, the implementation of the existing methods for topic trajectory identification is rarely available as usable software. In this paper, we present TopicTracker, a platform for topic trajectory identification and visualisation. The key of Topic Tracker is that it can represent the three facets of information together, given two kinds of input: a time-stamped topic profile consisting of the set of the underlying topics over time, and the evolution strength matrix among them: evolutionary pathways of dynamic topics, evolution states of the topics, and topic importance. TopicTracker is a publicly available software implemented using the R software.

cs.AI↗

arXiv2021

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $\mu$CO-HITS

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 categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose $\textit{ExpFinder}$, a new ensemble model for expert finding, that integrates a novel $N$-gram vector space model, denoted as $n$VSM, and a graph-based model, denoted as $\textit{$\mu$CO-HITS}$, that is a proposed variation of the CO-HITS algorithm. The key of $n$VSM is to exploit recent inverse document frequency weighting method for $N$-gram words and $\textit{ExpFinder}$ incorporates $n$VSM into $\textit{$\mu$CO-HITS}$ to achieve expert finding. We comprehensively evaluate $\textit{ExpFinder}$ on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that $\textit{ExpFinder}$ is a highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.

cs.IR↗

arXiv2020

Boosting House Price Predictions using Geo-Spatial Network Embedding

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 build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users' interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the Geo-Spatial Network Embedding (GSNE), that learns the embeddings of houses and various types of Points of Interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.

cs.LG↗

arXiv2020

Top-k Socio-Spatial Co-engaged Location Selection for Social Users

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, advertising, and group formation. To support such applications, in this paper, we formulate a new problem of identifying top-k Socio-Spatial co-engaged Location Selection (SSLS) for users in a social graph, that selects the best set of k locations from a large number of location candidates relating to the user and her friends. The selected locations should be (i) spatially and socially relevant to the user and her friends, and (ii) diversified in both spatially and socially to maximize the coverage of friends in the spatial space. This problem has been proved as NP-hard. To address the challenging problem, we first develop a branch-and-bound based Exact solution by designing some pruning strategies based on the derived bounds on diversity. To make the solution scalable for large datasets, we also develop an approximate solution by deriving the relaxed bounds and advanced termination rules to filter out insignificant intermediate results. To further accelerate the efficiency, we present one fast exact approach and a meta-heuristic approximate approach by avoiding the repeated computation of diversity at the running time. Finally, we have performed extensive experiments to evaluate the performance of our proposed models and algorithms against the adapted existing methods using four real-world large datasets.

cs.SI↗

arXiv2020

A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization

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 as the training proceeds to avoid over-fitting. There exist two major issues in this paradigm. Firstly, the validation set may hardly guarantee an unbiased estimate of generalization performance due to potential mismatching with test data. Secondly, training a DNN corresponds to solve a complex optimization problem, which is prone to getting trapped into inferior local optima and thus leads to undesired training results. To address these issues, we propose a novel DNN training framework. It generates multiple pairs of training and validation sets from the gross training set via random splitting, trains a DNN model of a pre-specified structure on each pair while making the useful knowledge (e.g., promising network parameters) obtained from one model training process to be transferred to other model training processes via multi-task optimization, and outputs the best, among all trained models, which has the overall best performance across the validation sets from all pairs. The knowledge transfer mechanism featured in this new framework can not only enhance training effectiveness by helping the model training process to escape from local optima but also improve on generalization performance via implicit regularization imposed on one model training process from other model training processes. We implement the proposed framework, parallelize the implementation on a GPU cluster, and apply it to train several widely used DNN models. Experimental results demonstrate the superiority of the proposed framework over the conventional training paradigm.

cs.NE↗

arXiv2019

Keyword Aware Influential Community Search in Large Attributed Graphs

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 or phrases). We first design the KICQ that facilitates users to issue an influential CS query intuitively by using a set of query terms, and predicates (AND or OR). In this context, we propose a novel word-embedding based similarity model that enables semantic community search, which substantially alleviates the limitations of exact keyword based community search. Next, we propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. Finally, we propose two efficient algorithms for searching influential communities in large attributed graphs. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.

cs.SI↗

arXiv2019

Location-Centered House Price Prediction: A Multi-Task Learning Approach

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 profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.

cs.LG↗

arXiv2018

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

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 paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi. This manuscript is under consideration for acceptance in TPLP.

cs.AI↗

arXiv2018

The Maximum Trajectory Coverage Query in Spatial Databases

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 applications. Supporting these services in emerging platforms will require more efficient query processing in trajectory databases. In this paper, we propose two new coverage queries for trajectory databases: (i) k Maximizing Reverse Range Search on Trajectories (kMaxRRST); and (ii) a Maximum k Coverage Range Search on Trajectories (MaxkCovRST). We propose a novel index structure, the Trajectory Quadtree (TQ-tree) that utilizes a quadtree to hierarchically organize trajectories into different quadtree nodes, and then applies a z-ordering to further organize the trajectories by spatial locality inside each node. This structure is highly effective in pruning the trajectory search space, which is of independent interest. By exploiting the TQ-tree data structure, we develop a divide-and-conquer approach to compute the trajectory "service value", and a best-first strategy to explore the trajectories using the appropriate upper bound on the service value to efficiently process a kMaxRRST query. Moreover, to solve the MaxkCovRST, which is a non-submodular NP-hard problem, we propose a greedy approximation which also exploits the TQ-tree. We evaluate our algorithms through an extensive experimental study on several real datasets, and demonstrate that our TQ-tree based algorithms outperform common baselines by two to three orders of magnitude.

cs.DB↗

arXiv2017

Reverse k Nearest Neighbor Search over Trajectories

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-Reverse k Nearest Neighbor Search over Trajectories (RkNNT), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, a RkNNT query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering a RkNNT query. Then we introduce a filter refinement framework for processing RkNNT queries using the proposed indexes. Next, we show how to use RkNNT to solve the optimal route planning problem MaxRkNNT (MinRkNNT), which is to search for the optimal route from a start location to an end location that could attract the maximum (or minimum) number of passengers based on a pre-defined travel distance threshold. Experiments on real datasets demonstrate the efficiency and scalability of our approaches. To the best of our best knowledge, this is the first work to study the RkNNT problem for route planning.

cs.DB↗

arXiv2016

Monitoring the Top-m Aggregation in a Sliding Window of Spatial Queries

In this paper, we propose and study the problem of top-m rank aggregation of spatial objects in streaming queries, where, given a set of objects O, a stream of spatial queries (kNN or range), the goal is to report m objects with the highest aggregate rank. The rank of an object w.r.t. an individual query is computed based on its distance from the query location, and the aggregate rank is computed from all of the individual rank orderings. Solutions to this fundamental problem can be used to monitor the popularity of spatial objects, which in turn can provide new analytical tools for spatial data. Our work draws inspiration from three different domains: rank aggregation, continuous queries and spatial databases. To the best of our knowledge, there is no prior work that considers all three problem domains in a single context. Our problem is different from the classical rank aggregation problem in the way that the rank of spatial objects are dependent on streaming queries whose locations are not known a priori, and is different from the problem of continuous spatial queries because new query locations can arrive in any region, but do not move. In order to solve this problem, we show how to upper and lower bound the rank of an object for any unseen query. Then we propose an approximation solution to continuously monitor the top-m objects efficiently, for which we design an Inverted Rank File (IRF) index to guarantee the error bound of the solution. In particular, we propose the notion of safe ranking to determine whether the current result is still valid or not when new queries arrive, and propose the notion of validation objects to limit the number of objects to update in the top-m results. We also propose an exact solution for applications where an approximate solution is not sufficient. Last, we conduct extensive experiments to verify the efficiency and effectiveness of our solutions.

cs.DB↗

arXiv2016

graphVizdb: A Scalable Platform for Interactive Large Graph Visualization

We present a novel platform for the interactive visualization of very large graphs. The platform enables the user to interact with the visualized graph in a way that is very similar to the exploration of maps at multiple levels. Our approach involves an offline preprocessing phase that builds the layout of the graph by assigning coordinates to its nodes with respect to a Euclidean plane. The respective points are indexed with a spatial data structure, i.e., an R-tree, and stored in a database. Multiple abstraction layers of the graph based on various criteria are also created offline, and they are indexed similarly so that the user can explore the dataset at different levels of granularity, depending on her particular needs. Then, our system translates user operations into simple and very efficient spatial operations (i.e., window queries) in the backend. This technique allows for a fine-grained access to very large graphs with extremely low latency and memory requirements and without compromising the functionality of the tool. Our web-based prototype supports three main operations: (1) interactive navigation, (2) multi-level exploration, and (3) keyword search on the graph metadata.

cs.HC↗

arXiv2016

A Hierarchical Aggregation Framework for Efficient Multilevel Visual Exploration and Analysis

Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for information abstraction and summarization. In this work, we present a generic model for personalized multilevel exploration and analysis over large dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale model. Considering different exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets.

cs.HC↗

arXiv2016

Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art

Data exploration and visualization systems are of great importance in the Big Data era. Exploring and visualizing very large datasets has become a major research challenge, of which scalability is a vital requirement. In this survey, we describe the major prerequisites and challenges that should be addressed by the modern exploration and visualization systems. Considering these challenges, we present how state-of-the-art approaches from the Database and Information Visualization communities attempt to handle them. Finally, we survey the systems developed by Semantic Web community in the context of the Web of Linked Data, and discuss to which extent these satisfy the contemporary requirements.

cs.HC↗

arXiv2015

Towards Scalable Visual Exploration of Very Large RDF Graphs

In this paper, we outline our work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large graphs. The proposed platform, called graphVizdb, is based on a novel technique for indexing and storing the graph. Particularly, the graph layout is indexed with a spatial data structure, i.e., an R-tree, and stored in a database. In runtime, user operations are translated into efficient spatial operations (i.e., window queries) in the backend.

cs.HC↗

arXiv2003

The Lowell Database Research Self Assessment

A group of senior database researchers gathers every few years to assess the state of database research and to point out problem areas that deserve additional focus. This report summarizes the discussion and conclusions of the sixth ad-hoc meeting held May 4-6, 2003 in Lowell, Mass. It observes that information management continues to be a critical component of most complex software systems. It recommends that database researchers increase focus on: integration of text, data, code, and streams; fusion of information from heterogeneous data sources; reasoning about uncertain data; unsupervised data mining for interesting correlations; information privacy; and self-adaptation and repair.

cs.DB↗