Computation Of Queries Research Articles

Efficient Top-K Continuous Query Processing Over Sliding Window Model (SWM) Method on Uncertain Data Stream

Query processing using the Uncertain Data Stream (UDS) can be complex in many technological scenarios due to inconsistencies, unclear information, and interpretation latency. As a result of both the sheer amount of data generated and the rate of change, traditional processing methods are in dire need of an upgrade. UDS consists of a finite set of states known as possible worlds (PW), and enhancing data organization can lead to more accurate extraction of user preferences. The number of possible world instances in UDS grows exponentially, making achieving Top-k query processing quickly a significant challenge. Different methods are available to handle Top-k queries in various types of UDS, and their key concerns include reducing duplicate scans of the entire dataset, enhancing uncertainty computation, and focusing on processing the latest tuple item entry. It appears that there have been limited studies conducted on the issue of UDS using the Sliding Window Model (SWM). The current approach for handling continuous queries on UDS within the SWM has proven to be ineffective, resulting in complex trade-offs between maximizing probability and generating high-scoring result sets. The challenge is to find the correct result list that satisfies a Top-k query predicate with scoring and probability. This study proposes a framework for processing Top-k queries for UDS using the sliding window model to improve efficiency. The study also discusses an improved optimization method for reducing computational redundancy in the context of the sliding window model and Top-k query processing. Overall, this research will significantly contribute to the Top-k computational query processing field.

Sequential Model-Based Diagnosis by Systematic Search (Abstract Reprint)

Model-based diagnosis aims at identifying the real cause of a system's malfunction based on a formal system model and observations of the system behavior. To discriminate between multiple fault hypotheses (diagnoses), sequential diagnosis approaches iteratively pose queries to an oracle to acquire additional knowledge about the diagnosed system. Depending on the system type, queries can capture, e.g., system tests, probes, measurements, or expert questions. As the determination of optimal queries is NP-hard, state-of-the-art sequential diagnosis methods rely on a myopic one-step-lookahead analysis which has proven to constitute a particularly favorable trade-off between computational efficiency and diagnostic effectivity. Yet, this solves only a part of the problem, as various sources of complexity, such as the reliance on costly reasoning services and large numbers of or not explicitly given query candidates, remain. To deal with such issues, existing approaches often make assumptions about the (i) type of diagnosed system, (ii) formalism to describe the system, (iii) inference engine, (iv) type of query to be of interest, (v) query quality criterion to be adopted, or (vi) diagnosis computation algorithm to be employed. Moreover, they (vii) often cannot deal with large or implicit query spaces or with expressive logics, or (viii) require inputs that cannot always be provided. As a remedy, we propose a novel one-step lookahead query computation technique for sequential diagnosis that overcomes the said issues of existing methods. Our approach (1) is based on a solid theory, (2) involves a systematic search for optimal queries, (3) can operate on implicit and huge query spaces, (4) allows for a two-stage optimization of queries (wrt. their number and cost), (5) is designed to reduce expensive logical inferences to a minimum, and (6) is generally applicable. The latter means that it can deal with any type of diagnosis problem as per Reiter's theory, is applicable with any monotonic knowledge representation language, can interact with a multitude of diagnosis engines and logical reasoners, and allows for a quality optimization of queries based on any of the common criteria in the literature. We extensively study the performance of the novel technique using a benchmark of real-world diagnosis problems. Our findings are that our approach enables the computation of optimal queries with hardly any delay, independently of the size and complexity of the considered benchmark problem. Moreover, it proves to be highly scalable, and it outperforms the state-of-the-art method in the domain of our benchmarks by orders of magnitude in terms of computation time while always returning a qualitatively as good or better query.

Fatigue and sleep quality in Chinese adults with connective tissue disease: A cross-sectional study.

This study aims to evaluate fatigue and sleep quality among adults with connective tissue disease (CTD), and the interrelationship. From April 2020 to February 2021, adult CTD patients hospitalized were invited to complete sociodemographic and disease-related data, Fatigue Severity Scale (FSS), and Pittsburgh Sleep Quality Index (PSQI), as well as laboratorydetection through computer query. Statistical analysis was performed using SPSS 22.0. A total of 363 patients with CTD were included in the study. Of which, 313 patients were accompaniedby single CTD (systemic lupus erythematosus (SLE) = 109, Sjogren's syndrome (SS) = 51, rheumatoidarthritis (RA) = 44, idiopathic inflammatory myopathies (IIM) = 36, Vasculitis = 17, systemic sclerosis (SSc) = 14, other = 42, respectively), and 50 patients had CTD ≥ 2. Compared with CTD = 1, patients with CTD ≥ 2 had longer disease duration, higher erythrocyte sedimentation rate, and higher IgG level (all P < 0.05). The prevalence of poor sleep quality in 363 CTD patients was 61.2%, among which, 59.1% for CTD = 1 and 74.0% for CTD ≥ 2, with significant difference (P = 0.045). Additionally, 75.5% experienced fatigue, and 75.4% for CTD = 1, 76.0% for CTD ≥ 2 (P = 0.927). Fatigue and sleep quality interacted in CTD patients (r = 0.236, P < 0.01; χ2 = 11.302, P = 0.001). Interestingly, no significant differences were found in the prevalence of fatigue and poor sleep quality among CTD subgroups, as well as the FSS score, the seven components of sleep quality and total PSQI score (P > 0.05). More than 3/5 CTD patients experience fatigue and poor sleep quality, and not different among CTD subgroups. Targeted interventions are needed to reduce fatigue, improve sleep quality, and ultimately improve the prognosis of patients with CTD.

AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query model

ABSTRACTAs an emerging knowledge representation model in the domain of knowledge graphs, geographic knowledge graph can take full advantage of semantic, spatial and temporal information to facilitate answering spatio-temporal questions and completing relations. However, the representation of geographic knowledge graphs still has issues such as the difficulty of unified heterogeneous spatio-temporal data modelling, weak ability to answer spatio-temporal queries for dynamic multiobjective problems, and low efficiency of graph querying. This paper presents a grid-augmented geographic knowledge graph (AugGKG) based on the GeoSOT global subdivision grid model and time slice subgraph architecture. AugGKG discretely normalizes the spatio-temporal data of the graph, which involves five types of nodes and two types of relations. By using the geo-hidden layer of the graph and geocoding algebraic operations, the AugGKG can quickly answer complex multiobjective spatio-temporal queries and complete implicit spatio-temporal relations. Compared with existing geographic knowledge graphs (YAGO, GeoKG and GEKG), the comparative experiments verified the obvious advantages of AugGKG in terms of uniformity of accuracy, completeness, and efficiency. Hence, AugGKG is expected to be regarded as an innovative and robust geographic knowledge graph that can perform fast computation and relation completion for complex spatio-temporal queries in future geospatial question answering applications.

Computational Review and Assessment of The Urban Heat Island Effect and Its Impact on Building Space Conditioning

This paper reviews and reports the recent progress and knowledge on the specific impact of the urban heat island (UHI) effect on building space conditioning for vulnerable housing where lack of air conditioning and fuel poverty causes indoor overheating, thus increasing vulnerability. Previous studies demonstrated that the increase of the ambient temperature due to UHI and heat waves impacts adversely cooling energy consumption of buildings and raises the peak electricity demand during summer and heat waves. Given the aging and dilapidated housing conditions in low-income communities, mostly of color and minorities, the economic burden of the cooling energy penalty induced by urban overheating is higher. However, literature on overheating is primarily driven by the physical characteristics of the building such as insulation, albedo, and envelope properties, and the Heat Vulnerability Index (HVI) by demographic data such as age, income, education largely remains isolated thus failing to capture the overall understanding of heat vulnerability and the role architects/urban designers can play in mitigation. Through a computational query review of the last fifteen years of publication, we are inquiring, about how UHI impacts building energy consumption in low-income and poor-quality housing and what role city and housing characteristics play in indoor overheating. Our study suggests, that in the US, due to segregated historic planning policies, low-income houses are often located in low tree canopy areas with varying urban typologies, and higher impervious material which substantially increases the air temperature thus determining energy consumption and anthropogenic heat release which contribute to present-day inequitable exposure to intra-urban heat. Both housing characteristics and the location of housing play a crucial role as similar housing will experience different exposure to intra-urban heat if not located in a heat canyon. Through this literature review, it became evident that there is a gap in the research that fails to connect building characteristics and overheating with heat vulnerability. Research involving UHI and heat vulnerability has continued to advance through energy analysis and mitigation studies, but future studies need to redefine the HVI index, especially by incorporating city and housing characteristics, which can help architects/urban designers make informed design decisions.

Pandemic retelling: What GoFundMe posts reveal about the socioeconomic context of COVID-19 death and bereavement among Latinx and Spanish-speaking communities in the United States

COVID-19 has had an outsized impact on Latinx communities in the United States, turning thousands of individuals into newly bereaved survivors. Despite an eagerness among scholars to document social inequities in COVID-19, data limitations prevent a deeper understanding of the socioeconomic context of COVID-19 death. This study explores the social relationships, economic factors, and emotional experiences that surround COVID-19 deaths among the members of Spanish-speaking Latinx communities in the United States, as retold in user-written posts on the GoFundMe crowdfunding platform. GoFundMe posts for funeral and related expenses in the aftermath of a COVID-19 death are an unexplored source of information about the impacts of COVID-19 mortality inequities on the health and well-being of bereaved individuals. First, we used computational query methods to systematically identify a sample of Spanish-language/bilingual GoFundMe posts from U.S. locations referencing the death of someone due to COVID-19. After excluding posts that did not meet search criteria, we used deductive and inductive coding to analyze a corpus of selected posts (n = 68). We found that a more complete story about the social context of COVID-19 deaths in Latinx and Spanish-speaking communities includes a focus on family roles and responsibilities, compounded financial hardship, risk of complicated grief, religiosity, and unexpected death. Our exploratory study highlights the need for further research into the contextualized health and well-being impacts of COVID-19 bereavement for structurally marginalized communities.

Sequential model-based diagnosis by systematic search

Model-based diagnosis aims at identifying the real cause of a system's malfunction based on a formal system model and observations of the system behavior. To discriminate between multiple fault hypotheses (diagnoses), sequential diagnosis approaches iteratively pose queries to an oracle to acquire additional knowledge about the diagnosed system. Depending on the system type, queries can capture, e.g., system tests, probes, measurements, or expert questions.As the determination of optimal queries is NP-hard, state-of-the-art sequential diagnosis methods rely on a myopic one-step-lookahead analysis which has proven to constitute a particularly favorable trade-off between computational efficiency and diagnostic effectivity. Yet, this solves only a part of the problem, as various sources of complexity, such as the reliance on costly reasoning services and large numbers of or not explicitly given query candidates, remain.To deal with such issues, existing approaches often make assumptions about the (i) type of diagnosed system, (ii) formalism to describe the system, (iii) inference engine, (iv) type of query to be of interest, (v) query quality criterion to be adopted, or (vi) diagnosis computation algorithm to be employed. Moreover, they (vii) often cannot deal with large or implicit query spaces or with expressive logics, or (viii) require inputs that cannot always be provided.As a remedy, we propose a novel one-step lookahead query computation technique for sequential diagnosis that overcomes the said issues of existing methods. Our approach (1) is based on a solid theory, (2) involves a systematic search for optimal queries, (3) can operate on implicit and huge query spaces, (4) allows for a two-stage optimization of queries (wrt. their number and cost), (5) is designed to reduce expensive logical inferences to a minimum, and (6) is generally applicable. The latter means that it can deal with any type of diagnosis problem as per Reiter's theory, is applicable with any monotonic knowledge representation language, can interact with a multitude of diagnosis engines and logical reasoners, and allows for a quality optimization of queries based on any of the common criteria in the literature.We extensively study the performance of the novel technique using a benchmark of real-world diagnosis problems. Our findings are that our approach enables the computation of optimal queries with hardly any delay, independently of the size and complexity of the considered benchmark problem. Moreover, it proves to be highly scalable, and it outperforms the state-of-the-art method in the domain of our benchmarks by orders of magnitude in terms of computation time while always returning a qualitatively as good or better query.

Multiwinner Voting with Possibly Unavailable Candidates

Selecting a committee that meets diversity and proportionality criteria is a challenging endeavor that has been studied extensively in recent years. This task becomes even more challenging when some of the selected candidates decline the invitation to join the committee. Since the unavailability of one candidate may impact the rest of the selection, inviting all candidates at the same time may lead to a suboptimal committee. Instead, invitations should be sequential and conditional on which candidates invited so far accepted the invitation: the solution to the committee selection problem is a query policy. If invitation queries are binding, they should be safe: one should not query a candidate without being sure that whatever the set of available candidates possible at that stage, her inclusion will not jeopardize committee optimality. Assuming approval-based inputs, we characterize the set of rules for which a safe query exists at every stage. In order to parallelize the invitation process, we investigate the computation of safe parallel queries, and show that it is often hard. We also study the existence of safe parallel queries with respect to proportionality axioms such as extended justified representation.

Abstract 3150: Computational cohort discovery and coordinator-led validation to navigate potential research candidates to clinical trials

Abstract The purpose of this study was to improve the identification and solicitation of human subject candidates to augment patient enrollment to clinical trials. In the past, our enrollment efforts involved manual mechanisms that began at the time that cancer patients were presented for care, most commonly at our chemotherapy clinics. We reasoned that computational review of pathology records may be far more efficient, allowing us to screen larger numbers of patients, and may allow us to intercept patients at the time of diagnosis, rather than time of care. We thus implemented a new workflow: (1) computational query of pathology database to filter candidates based on key eligibility criteria; (2) validation of the query using a clinical research coordinator, who can then (3) navigate the patient to the trial. As a proof of concept, starting in August of 2022, we selected two gastrointestinal oncology trials, the COBRA and PACES trial, based on trial eligibility that could be easily extracted using pathologic criteria, and because these trials had low enrollment. We designed computational queries to extract cohorts of potential patients whose surgery dates and pathology staging identified potential clinical trial candidates. A coordinator performed chart-review to validate these candidates. The trial coordinator then contacted the patient’s surgical oncologist, facilitated referral to a medical oncologist, and tracked the number of patients approached and consented. Whereas in a 12-month period preceding this new workflow, we had 0 and 1 patients approached for the PACES and COBRA trial, in the 3-month period following the new intervention, we had 8 and 2 patients approached. These data suggest computational cohort building and subsequent navigation have the potential to improve enrollment onto clinical trials and motivate extension of this work to additional trials. Table(s) Trial # of patients approached pre-intervention Computational Criteria Meeting computational criteria Coordinator validation criteria # of patients validated by coordinator # of patients connected with medical oncologist # of patients approached post-intervention PACES 0 Colorectal cancer, stage T0-T3,N0-1 Date Aug 2021 – July 2022 23 Facility location, No evidence of disease on colonoscopy performed 120 -456 days post-surgery, No chemotherapy, No other malignancy 18 8 8 COBRA 1 Colorectal cancer pT3N0resection date August 2022 – October 2022 18 ≥ 12 lymph nodes assessedPatient locationIf the subject appropriate for active surveillanceSurgery within 14-60 days of randomization 4 0 2 Citation Format: Yonah Ziemba, Kaitlin Parnahay, Paul Ward, Nalan Yurtsever, Sharon Fox, Cheryl Schleicher, Priya Singh-Shiwsankar, Vincent Vinciguerra, Joseph Herman, James M. Crawford, Daniel A. King. Computational cohort discovery and coordinator-led validation to navigate potential research candidates to clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3150.

Trust and QoS-Driven Query Service Provisioning Using Optimization

The growing advancements with the Internet of Things (IoT) devices handle an enormous amount of data collected from various applications like healthcare, vehicle-based communication, and smart city. This research analyses cloud-based privacy preservation over the smart city based on query computation. However, there is a lack of resources to handle the incoming data and maintain them with higher privacy and security. Therefore, a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment. A querying service model is proposed to handle the incoming data collected from various environments as the data is not so trusted and highly sensitive towards vulnerability. If handling privacy, other inter-connected metrics like efficiency are also essential, which must be considered to fulfil the privacy requirements. Therefore, this work provides a query-based service model and clusters the query to measure the relevance of frequently generated queries. Here, a Bag of Query (BoQ) model is designed to collect the query from various sources. Validation is done with a descriptive service provisioning model to cluster the query and extract the query’s summary to get the final results. The processed data is preserved over the cloud storage system and optimized using an improved Grey Wolf Optimizer (GWO). It is used to attain global and local solutions regarding privacy preservation. The iterative data is evaluated without any over-fitting issues and computational complexity due to the tremendous data handling process. Based on this analysis, metrics like privacy, efficiency, computational complexity, the error rate is analyzed. The simulation is done with a MATLAB 2020a environment. The proposed model gives a better trade-off in contrast to existing approaches.

SkyFlow: Heterogeneous streaming for skyline computation using FlowGraph and SYCL

The skyline is an optimization operator widely used for multi-criteria decision making. It allows minimizing an n-dimensional dataset into its smallest subset. In this work we present SkyFlow, the first heterogeneous CPU+GPU graph-based engine for skyline computation on a stream of data queries. Two data flow approaches, Coarse-grained and Fine-grained, have been proposed for different streaming scenarios. Coarse-grained aims to keep in parallel the computation of two queries using a hybrid solution with two state-of-the-art skyline algorithms: one optimized for CPU and another for GPU. We also propose a model to estimate at runtime the computation time of any arriving data query. This estimation is used by a heuristic to schedule the data query on the device queue in which it will finish earlier. On the other hand, Fine-grained splits one query computation between CPU and GPU. An experimental evaluation using as target architecture a heterogeneous system comprised of a multicore CPU and an integrated GPU for different streaming scenarios and datasets, reveals that our heterogeneous CPU+GPU approaches always outperform previous only-CPU and only-GPU state-of-the-art implementations up to 6.86×and 5.19×, respectively, and they fall below 6% of ideal peak performance at most. We also evaluate Coarse-grained vs Fine-Grained finding that each approach is better suited to different streaming scenarios.

SPCS: Strong Privacy-Preserving-Constrained Shortest Distance Queries on Encrypted Graphs

A constrained shortest distance (CSD) query calculates the shortest distance between two vertices of a graph and places constraints on certain factors so as not to exceed corresponding thresholds. With the development of cloud computing, outsourcing graph data storage and the computation of CSD queries to a cloud platform (CP) are an attractive choice for graph owners; however, this choice is accompanied by well-known privacy issues. Certain privacy-preserving schemes have been proposed to support CSD queries on encrypted graphs, but all such schemes consider only single-CSD queries, even though, in practical applications, users may need to set two or more constraints when executing the shortest distance query. Additionally, existing schemes disclose too much private information to the CP. To address these considerable problems, we propose a strong privacy-preserving CSD query scheme called SPCS, which realizes accurate double-CSD queries without disclosing any critical private information. We prove that SPCS does not reveal any private information to the CP except for the number of vertices and conduct numerous experiments on real-world data sets to test this scheme’s efficiency. The experimental results show that SPCS is practical, especially in its computational efficiency in the graph-encryption phase, which is higher than that of available, state-of-the-art schemes.

DIMBA: discretely masked black-box attack in single object tracking

The adversarial attack can force a CNN-based model to produce an incorrect output by craftily manipulating human-imperceptible input. Exploring such perturbations can help us gain a deeper understanding of the vulnerability of neural networks, and provide robustness to deep learning against miscellaneous adversaries. Despite extensive studies focusing on the robustness of image, audio, and NLP, works on adversarial examples of visual object tracking—especially in a black-box manner—are quite lacking. In this paper, we propose a novel adversarial attack method to generate noises for single object tracking under black-box settings, where perturbations are merely added on initialized frames of tracking sequences, which is difficult to be noticed from the perspective of a whole video clip. Specifically, we divide our algorithm into three components and exploit reinforcement learning for localizing important frame patches precisely while reducing unnecessary computational queries overhead. Compared to existing techniques, our method requires less time to perturb videos, but to manipulate competitive or even better adversarial performance. We test our algorithm in both long-term and short-term datasets, including OTB100, VOT2018, UAV123, and LaSOT. Extensive experiments demonstrate the effectiveness of our method on three mainstream types of trackers: discrimination, Siamese-based, and reinforcement learning-based trackers. We release our attack tool, DIMBA, via GitHub https://github.com/TrustAI/DIMBA for use by the community.

One step at a time: An efficient approach to query-based ontology debugging

When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for a semi-automatic fault localization in ontologies have been proposed and extensively studied. One class of these approaches involve a human expert who provides answers to system-generated queries about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting expert.In this work, we demonstrate that these assumptions might not always be adequate and discuss consequences of their violations. In particular, we characterize a range of expert types with different query answering behavior and show that existing approaches are far from achieving optimal efficiency for all of them. In addition, we find that the cost metric adopted by state-of-the-art techniques might not always be realistic and that a change of metric has a decisive impact on the best choice of query answering strategy. As a remedy, we suggest a new – and simpler – type of expert question that leads to a stable fault localization performance for all analyzed expert types and effort metrics, and has numerous further advantages over existing techniques. Moreover, we present an algorithm which computes and optimizes this new query type in worst-case polynomial time and which is fully compatible with existing concepts (e.g., query selection heuristics) and infrastructure (e.g., debugging user interfaces) in the field.Comprehensive experiments on faulty real-world ontologies attest that the new querying method is substantially and statistically significantly superior to existing techniques both in terms of the number of necessary expert interactions and in terms of the query computation time. We find that relying on the new querying method can save an interacting expert more than 80% of their work, and can reduce the expert’s waiting time for the next query by more than three orders of magnitude. Beside these findings, we demonstrate that the efficiency of existing query-based tools can be significantly boosted by suggesting an appropriate query answering strategy to an expert; we also make recommendations in this regard. Further, we suggest optimal configurations of a debugger for situations where the new type of query is used.Remarkably, the proposed approach is not only applicable to ontologies, but to any monotonic knowledge representation language, and can even be adopted to solve general model-based diagnosis problems expressible using Reiter’s theory.

Efficient Range and kNN Twin Subsequence Search in Time Series

Analyzing time series data is crucial for many applications. In particular, subsequence search refers to finding subsequences within an input time series T that are similar to a query sequence Q. Existing subsequence search approaches typically employ Euclidean distance or Dynamic Time Warping as similarity measures and address range queries. In this paper, we focus on Chebyshev distance, which is the largest difference between each individual pair of points across the entire length of two compared subsequences. We call such similar pairstwins. We first show how existing time series indices can be extended to perform twin subsequence search. Then, we introduce TS-Index, a novel index tailored to the computation of twin subsequence search queries. Moreover, given that specifying a distance threshold is often not straightforward, we show how TS-Index can also be used to evaluate kNN queries. Our extensive experimental evaluation compares these approaches using real time series datasets. The results demonstrate that TS-Index can retrieve twin subsequences faster than all other methods under various conditions.

Spread Estimation With Non-Duplicate Sampling in High-Speed Networks

Per-flow spread measurement in high-speed networks has many practical applications. It is a more difficult problem than the traditional per-flow size measurement. Most prior work is based on sketches, focusing on reducing their space requirements in order to fit in on-chip cache memory. This design allows the measurement to be performed at the line rate, but it suffers from expensive computation for spread queries (unsuitable for online operations) and large errors in spread estimation for small flows. This paper complements the prior art with a new spread estimator design based on an on-chip/off-chip model. By storing traffic statistics in off-chip memory, our new design faces a key technical challenge to design an efficient online module of non-duplicate sampling that cuts down the off-chip memory access. We first propose a two-stage solution for non-duplicate sampling, which is efficient but cannot handle well a sampling probability that is either too small or too big. We then address this limitation through a three-stage solution that is more space-efficient. Our analysis shows that the proposed spread estimator is highly configurable for a variety of probabilistic performance guarantees. We implement our spread estimator in hardware using FPGA. The experiment results based on real Internet traffic traces show that our estimator produces spread estimation with much better accuracy than the prior art, reducing the mean relative (absolute) error by about one order of magnitude. Moreover, it increases the query throughput by around three orders of magnitude, making it suitable for supporting online queries in real time.

Efficient computation of spatial queries over points stored in k2-tree compact data structures

We present efficient algorithms to compute two spatial queries over points stored in compact data structures. The former is the K-Nearest Neighbors Query (KNN) which given a point q gets the K-nearest points to q. The latter query is the K-Closest Pair Query (KCPQ), which obtains the K-pairs of closest neighbors between two set of points R and S on the same spatial plane. There are several efficient implementations of these queries, which work mainly with data stored in secondary memory. However, these implementations do not scale well over large datasets. Our algorithms compute the queries over large datasets of points stored in compact data structures, in main memory. Compact data structures are structures that allow efficiently storage data in main memory and query them in their compressed form. We use the k2-tree compact structure to represent points of interest. Through experimentation over synthetic and real datasets, we show that by using the k2-tree we can work with large datasets in main memory, and that the KNN and KCPQ spatial data queries can be efficiently computed over the compact data structures. We also implement a JAVA library that is available for the academic and industrial community.

A Method for Processing Top-k Continuous Query on Uncertain Data Stream in Sliding Window Model

The data stream is a series of data generated at sequential time from different sources. Processing such data is very important in many contemporary applications such as sensor networks, RFID technology, mobile computing and many more. The huge amount data generated and frequent changes in a short time makes the conventional processing methods insufficient. The Sliding Window Model (SWM) was introduced by Datar et. al to handle this problem. Avoiding multiple scans of the whole data sets, optimizing memory usage, and processing only the most recent tuple are the main challenges. The number of possible world instances grows exponentially in uncertain data and it is highly difficult to comprehend what it takes to meet Top-k query processing in the shortest amount of time. Following the generation of rules and the probability theory of this model, a framework was anticipated to sustain top-k processing algorithm over the SWM approach until the candidates expired. Based on the literature review study, none of the existing work have been made to tackle the issue arises from the top-k query processing of the possible world instance of the uncertain data streams within the SWM. The major issue resulted from these scenarios need to be addressed especially in the computation redundancy area that contributed to the increases of computational cost within the SWM. Therefore, the main objective of this research work is to propose the top-k query processing methods over uncertain data streams in SWM utilizing the score and the Possible World (PW) setting. In this study, a novel expiration and object indexing method is introduced to address the computational redundancy issues. We believed the proposed method can reduce computational costs and by managing insertion and exit policy on the right tuple candidates within a specified window frame. This research work will contribute to the area of computational query processing.

Optimizing Multi-Query Evaluation in Federated RDF Systems

This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of SPARQL 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of SPARQL 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable.

Efficient computation of deletion-robust k-coverage queries

Extracting a controllable subset from a large-scale dataset so that users can fully understand the entire dataset is a significant topic for multicriteria decision making. In recent years, this problem has been widely studied, and various query models have been proposed, such as top-k, skyline, k-regret and k-coverage queries. Among these models, the k-coverage query is an ideal query method; this model has stability, scale invariance and high traversal efficiency. However, current methods including k-coverage queries cannot deal with deleting some points from the dataset while providing an effective solution set efficiently. In this paper, we study the robustness of k-coverage queries in two cases involving the dynamic deletion of data points. The first case is when it is assumed that the whole dataset can be obtained in advance, while the second is when the data points arrive in a stream. For a centralized dataset, we introduce a sieving mechanism and use a precalculated threshold to filter a coreset from the entire dataset. Then, the k-coverage query can be carried out on this small coreset instead of the entire dataset, and we propose a threshold-based k-coverage query algorithm, which greatly accelerates query processing. For a streaming dataset, a special chain structure is adopted. Furthermore, a single-pass streaming algorithm named Robust-Sieving is proposed. Moreover, the coreset-based method is extended to answer the problem. In addition, sampling techniques are adopted to accelerate query processing under these two circumstances. Extensive experiments verify the effectiveness of our proposed Robust-Sieving algorithm and the coreset-based algorithms with or without sampling.