Showing papers by "Nikos Mamoulis published in 2019"

HHMF: hidden hierarchical matrix factorization for recommender systems

Abstract: Matrix factorization (MF) is one of the most powerful techniques used in recommender systems. MF models the (user, item) interactions behind historical explicit or implicit ratings. Standard MF does not capture the hierarchical structural correlations, such as publisher and advertiser in advertisement recommender systems, or the taxonomy (e.g., tracks, albums, artists, genres) in music recommender systems. There are a few hierarchical MF approaches, but they require the hierarchical structures to be known beforehand. In this paper, we propose a Hidden Hierarchical Matrix Factorization (HHMF) technique, which learns the hidden hierarchical structure from the user-item rating records. HHMF does not require the prior knowledge of hierarchical structure; hence, as opposed to existing hierarchical MF methods, HHMF can be applied when this information is either explicit or implicit. According to our extensive experiments, HHMF outperforms existing methods, demonstrating that the discovery of latent hierarchical structures indeed improves the quality of recommendation.

Parallel In-Memory Evaluation of Spatial Joins

Abstract: We study the in-memory and parallel evaluation of spatial joins, by tuning a classic partitioning based algorithm. Our study shows that, compared to a straightforward implementation of the algorithm, performance can be improved significantly. We also show how to select appropriate partitioning parameters based on data statistics, in order to tune the algorithm for the given join inputs. Our parallel implementation scales gracefully with the number of threads reducing the cost of the join to at most one second even for join inputs with tens of millions of rectangles.

Spatial joins: what's next?

Abstract: The spatial join is a popular operation in spatial database systems and its evaluation is a well-studied problem. This paper reviews research and recent trends on spatial join evaluation. The complexity of different data types, the consideration of different join predicates, the use of modern commodity hardware, and support for parallel processing open the road to a number of interesting directions for future research, some of which we outline in the paper.

Evaluating pattern matching queries for spatial databases

Abstract: In this paper, we study the spatial pattern matching (SPM) query. Given a set D of spatial objects (e.g., houses and shops), each with a textual description, we aim at finding all combinations of objects from D that match a user-defined spatial patternP. A pattern P is a graph whose vertices represent spatial objects, and edges denote distance relationships between them. The SPM query returns the instances that satisfy P. An example of P can be “a house within 10-min walk from a school, which is at least 2 km away from a hospital.” The SPM query can benefit users such as house buyers, urban planners, and archeologists. We prove that answering such queries is computationally intractable and propose two efficient algorithms for their evaluation. Moreover, we study efficient solutions to address two related problems of the SPM: (1) find top-k matches that are close to a query location and (2) return partial matches for a query pattern. Experiments and case studies on real datasets show that our proposed solutions are highly effective and efficient.

SRX: efficient management of spatial RDF data

Abstract: We present a general encoding scheme for the efficient management of spatial RDF data. The scheme approximates the geometries of the RDF entities inside their (integer) IDs and can be used, along with several operators and optimizations we introduce, to accelerate queries with spatial predicates and to re-encode entities dynamically in case of updates. We implement our ideas in SRX, a system built on top of the popular RDF-3X system. SRX extends RDF-3X with support for three types of spatial queries: range selections (e.g., find entities within a given polygon), spatial joins (e.g., find pairs of entities whose locations are close to each other), and spatial k-nearest neighbors (e.g., find the three closest entities from a given location). We evaluate SRX on spatial queries and updates with real RDF data, and we also compare its performance with the latest versions of three popular RDF stores. The results show SRX ’s superior performance over the competitors; compared to RDF-3X, SRX improves its performance for queries with spatial predicates while incurring little overhead during updates.

An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms

Abstract: In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms. Previous works conduct the personalized recommendation of tasks to workers via supervised learning methods. However, the majority of them only consider the benefit of either workers or requesters independently. In addition, they cannot handle the dynamic environment and may produce sub-optimal results. To address these issues, we utilize Deep Q-Network (DQN), an RL-based method combined with a neural network to estimate the expected long-term return of recommending a task. DQN inherently considers the immediate and future reward simultaneously and can be updated in real-time to deal with evolving data and dynamic changes. Furthermore, we design two DQNs that capture the benefit of both workers and requesters and maximize the profit of the platform. To learn value functions in DQN effectively, we also propose novel state representations, carefully design the computation of Q values, and predict transition probabilities and future states. Experiments on synthetic and real datasets demonstrate the superior performance of our framework.

Parallel In-Memory Evaluation of Spatial Joins

Abstract: The spatial join is a popular operation in spatial database systems and its evaluation is a well-studied problem. As main memories become bigger and faster and commodity hardware supports parallel processing, there is a need to revamp classic join algorithms which have been designed for I/O-bound processing. In view of this, we study the in-memory and parallel evaluation of spatial joins, by re-designing a classic partitioning-based algorithm to consider alternative approaches for space partitioning. Our study shows that, compared to a straightforward implementation of the algorithm, our tuning can improve performance significantly. We also show how to select appropriate partitioning parameters based on data statistics, in order to tune the algorithm for the given join inputs. Our parallel implementation scales gracefully with the number of threads reducing the cost of the join to at most one second even for join inputs with tens of millions of rectangles.

A General Early-Stopping Module for Crowdsourced Ranking

Abstract: Crowdsourcing can be used to determine a total order for an object set (e.g., the top-10 NBA players) based on crowd opinions. This ranking problem is often decomposed into a set of microtasks (e.g., pairwise comparisons). These microtasks are passed to a large number of workers and their answers are aggregated to infer the ranking. The number of microtasks depends on the budget allocated for the problem. Intuitively, the higher the number of microtask answers, the more accurate the ranking becomes. However, it is often hard to decide the budget required for an accurate ranking. We study how a ranking process can be terminated early, and yet achieve a high-quality ranking and great savings in the budget. We use statistical tools to estimate the quality of the ranking result at any stage of the crowdsourcing process and terminate the process as soon as the desired quality is achieved. Our proposed early-stopping module can be seamlessly integrated with most existing inference algorithms and task assignment methods. We conduct extensive experiments and show that our early-stopping module is better than other existing general stopping criteria. We also implement a prototype system to demonstrate the usability and effectiveness of our approach in practice.