João B. Rocha-Junior

Efficient processing of top-k spatial keyword queries

Given a spatial location and a set of keywords, a top-k spatial keyword query returns the k best spatio-textual objects ranked according to their proximity to the query location and relevance to the query keywords. There are many applications handling huge amounts of geotagged data, such as Twitter and Flickr, that can benefit from this query. Unfortunately, the state-of-the-art approaches require non-negligible processing cost that incurs in long response time. In this paper, we propose a novel index to improve the performance of top-k spatial keyword queries named Spatial Inverted Index (S2I). Our index maps each distinct term to a set of objects containing the term. The objects are stored differently according to the document frequency of the term and can be retrieved efficiently in decreasing order of keyword relevance and spatial proximity. Moreover, we present algorithms that exploit S2I to process top-k spatial keyword queries efficiently. Finally, we show through extensive experiments that our approach outperforms the state-of-the-art approaches in terms of update and query cost.

Top-k spatial keyword queries on road networks

With the popularization of GPS-enabled devices there is an increasing interest for location-based queries. In this context, one interesting problem is processing top-k spatial keyword queries. Given a set of objects with a textual description (e.g., menu of a restaurant), a query location (latitude and longitude), and a set of query keywords, a top-k spatial keyword query returns the k best objects ranked in terms of both distance to the query location and textual relevance to the query keywords. So far, the research on this problem has assumed Euclidean space. In order to process such queries efficiently, spatio-textual indexes combining R-trees and inverted files are employed. However, for most real applications, the distance between the objects and query location is constrained by a road network (shortest path) and cannot be computed efficiently using R-trees. In this paper, we address, for the first time, the challenging problem of processing top-k spatial keyword queries on road networks where the distance between the query location and the spatial object is the shortest path. We formalize the new query type, and present novel indexing structures and algorithms that are able to process such queries efficiently. Finally, we perform an experimental evaluation that shows the efficiency of our approach.

Efficient processing of top-k spatial preference queries

Top-k spatial preference queries return a ranked set of the k best data objects based on the scores of feature objects in their spatial neighborhood. Despite the wide range of location-based applications that rely on spatial preference queries, existing algorithms incur non-negligible processing cost resulting in high response time. The reason is that computing the score of a data object requires examining its spatial neighborhood to find the feature object with highest score. In this paper, we propose a novel technique to speed up the performance of top-k spatial preference queries. To this end, we propose a mapping of pairs of data and feature objects to a distance-score space, which in turn allows us to identify and materialize the minimal subset of pairs that is sufficient to answer any spatial preference query. Furthermore, we present a novel algorithm that improves query processing performance by avoiding examining the spatial neighborhood of the data objects during query execution. In addition, we propose an efficient algorithm for materialization and we describe useful properties that reduce the cost of maintenance. We show through extensive experiments that our approach significantly reduces the number of I/Os and execution time compared to the state-of-the-art algorithms for different setups.

AGiDS: A Grid-Based Strategy for Distributed Skyline Query Processing

Skyline queries help users make intelligent decisions over complex data, where different and often conflicting criteria are considered. A challenging problem is to support skyline queries in distributed environments, where data is scattered over independent sources. The query response time of skyline processing over distributed data depends on the amount of transferred data and the query processing cost at each server. In this paper, we propose AGiDS, a framework for efficient skyline processing over distributed data. Our approach reduces significantly the amount of transferred data, by using a grid-based data summary that captures the data distribution on each server. AGiDS consists of two phases to compute the result: in the first phase the querying server gathers the grid-based summary, whereas in the second phase a skyline request is sent only to the servers that may contribute to the skyline result set asking only for the points of non-dominated regions. We provide an experimental evaluation showing that our approach performs efficiently and outperforms existing techniques.

Efficient execution plans for distributed skyline query processing

In this paper, we study the generation of efficient execution plans for skyline query processing in large-scale distributed environments. In such a setting, each server stores autonomously a fraction of the data, thus all servers need to process the skyline query. An execution plan defines the order in which the individual skyline queries are processed on different servers, and influences the performance of query processing. Querying servers consecutively reduces the amount of transferred data and the number of queried servers, since skyline points obtained by one server prune points in the subsequent servers, but also increases the latency of the system. To address this trade-off, we introduce a novel framework, called SkyPlan, for processing distributed skyline queries that generates execution plans aiming at optimizing the performance of query processing. Thus, we quantify the gain of querying consecutively different servers. Then, execution plans are generated that maximize the overall gain, while also taking into account additional objectives, such as bounding the maximum number of hops required for the query or balancing the load on different servers fairly. Finally, we present an algorithm for distributed processing based on the generated plan that continuously refines the execution plan during in-network processing. Our framework consistently outperforms the state-of-the-art algorithm.

Investigating factors that affect the human perception on god class detection: an analysis based on a family of four controlled experiments

ContextEvaluation of design problems in object oriented systems, which we call code smells, is mostly a human-based task. Several studies have investigated the impact of code smells in practice. Studies focusing on human identification of code smells have shown low agreement among developers. Unfortunately, those studies do not attempt to investigate the reasons behind this phenomenon.ObjectiveThis paper aims to investigate factors affecting human perception of code smells. Specifically, it focuses on factors affecting god class detection, one of the most known code smells.MethodThe investigation encompassed a family of four controlled experiments, covering potential factors affecting human detection of code smells. The method is incremental. In other words, each experiment produces insights to the next one. This allows the investigators to control specific factors affecting the agreement on god class detection. The factors addressed in this study are: i) developer experience, ii) developer knowledge, iii) developer training, iv) tool support for design comprehension, and v) software size.ResultOur findings show that tool support for design comprehension is the only factor that does not affect the human perception of god class. The other factors impact this perception in some way.ConclusionThe area still needs more investigation and discussion on what we call the code smell conceptualization problem, to ensure similar criteria and thresholds on human-based code smell detection.

TwitterSensing: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications

Modern cities are subject to periodic or unexpected critical events, which may bring economic losses or even put people in danger. When some monitoring systems based on wireless sensor networks are deployed, sensing and transmission configurations of sensor nodes may be adjusted exploiting the relevance of the considered events, but efficient detection and classification of events of interest may be hard to achieve. In Smart City environments, several people spontaneously post information in social media about some event that is being observed and such information may be mined and processed for detection and classification of critical events. This article proposes an integrated approach to detect and classify events of interest posted in social media, notably in Twitter, and the assignment of sensing priorities to source nodes. By doing so, wireless sensor networks deployed in Smart City scenarios can be optimized for higher efficiency when monitoring areas under the influence of the detected events.

A systematic review on the code smell effect

Abstract Context: Code smell is a term commonly used to describe potential problems in the design of software. The concept is well accepted by the software engineering community. However, some studies have presented divergent findings about the usefulness of the smell concept as a tool to support software development tasks. The reasons of these divergences have not been considered because the studies are presented independently. Objective: To synthesize current knowledge related to the usefulness of the smell concept. We focused on empirical studies investigating how smells impact the software development, the code smell effect. Method: A systematic review about the smell effect is carried out. We grouped the primary studies findings in a thematic map. Result: The smell concept does not support the evaluation of quality design in practice activities of software development. There is no strong evidence correlating smells and some important software development attributes, such as effort in maintenance. Moreover, the studies point out that human agreement on smell detection is low. Conclusion: In order to improve analysis on the subject, the area needs to better outline: (i) factors affecting human evaluation of smells; and (ii) a classification of types of smells, grouping them according to relevant characteristics.

Efficient Processing of Spatio-Temporal-Textual Queries

Devices with built-in GPS (e.g. smartphones) are producing a huge amount of data objects with spatial, temporal and textual information. For example, a significant part of Twitter messages sent from smartphones has spatial location (latitude and longitude), temporal information (timestamp) and textual information (the message itself). Therefore, there is a growing interest for new approaches that are able to select the data objects that are spatially, temporally and textually relevant from huge datasets. In this paper, we specify the spatio-temporal-textual query that returns the relevant data objects considering these three criteria simultaneously, presenting new indexes and algorithms to process such query efficiently. The proposed approaches are evaluated taking real datasets, potentially providing more accurate results.