Query Accuracy Research Articles

I-ViSE: Interactive Video Surveillance as an Edge Service Using Unsupervised Feature Queries

Situation awareness (SAW) is essential for many mission-critical applications. However, SAW is challenging when trying to immediately identify objects of interest or focus on suspicious activities from thousands of video frames. This article develops a queryable system to instantly select interesting content. While face recognition technology is mature, in many scenarios, such as public safety monitoring, the features of objects of interest may be much more complicated than face features. In addition, human operators may not be always able to provide a descriptive, simple, and accurate query. Actually, it is more often that there are only rough, general descriptions of certain suspicious objects or accidents. This article proposes interactive video surveillance as an edge service (I-ViSE) based on unsupervised feature queries. Adopting unsupervised methods that do not reveal any private information, the I-ViSE scheme utilizes general features of a human body and color of clothes. An I-ViSE prototype is built following the edge-fog computing paradigm and the experimental results verified the I-ViSE scheme meets the design goal of scene recognition and target analysis in less than 2 s.

HVS

Approximate nearest neighbor search (ANNS) is a fundamental problem that has a wide range of applications in information retrieval and data mining. Among state-of-the-art in-memory ANNS methods, graph-based methods have attracted particular interest owing to their superior efficiency and query accuracy. Most of these methods focus on the selection of edges to shorten the search path, but do not pay much attention to the computational cost at each hop. To reduce the cost, we propose a novel graph structure called HVS. HVS has a hierarchical structure of multiple layers that corresponds to a series of subspace divisions in a coarse-to-fine manner. In addition, we utilize a virtual Voronoi diagram in each layer to accelerate the search. By traversing Voronoi cells, HVS can reach the nearest neighbors of a given query efficiently, resulting in a reduction in the total search cost. Experiments confirm that HVS is superior to other state-of-the-art graph-based methods.

MP-RW-LSH

Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-Sensitive Hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS- L 2 , a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in L 1 distance, and show that it achieves a better tradeoff between scalability and query efficiency than all existing LSH-based solutions. We also explain why a state-of-the-art ANNS -L 1 solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. Finally, as a use case, we construct, using MP-RW-LSH as the underlying "ANNS- L 1 engine", a new ANNS-E (E for edit distance) solution that beats the state of the art.

PoBery: Possibly-complete Big Data Queries with Probabilistic Data Placement and Scanning

In big data query processing, there is a trade-off between query accuracy and query efficiency, for example, sampling query approaches trade-off query completeness for efficiency. In this article, we argue that query performance can be significantly improved by slightly losing the possibility of query completeness, that is, the chance that a query is complete. To quantify the possibility, we define a new concept, Probability of query Completeness (hereinafter referred to as PC). For example, If a query is executed 100 times, PC = 0.95 guarantees that there are no more than 5 incomplete results among 100 results. Leveraging the probabilistic data placement and scanning, we trade off PC for query performance. In the article, we propose PoBery (POssibly-complete Big data quERY), a method that supports neither complete queries nor incomplete queries, but possibly-complete queries. The experimental results conducted on HiBench prove that PoBery can significantly accelerate queries while ensuring the PC. Specifically, it is guaranteed that the percentage of complete queries is larger than the given PC confidence. Through comparison with state-of-the-art key-value stores, we show that while Drill-based PoBery performs as fast as Drill on complete queries, it is 1.7 ×, 1.1 ×, and 1.5 × faster on average than Drill, Impala, and Hive, respectively, on possibly-complete queries.

DMatrix: Toward fast and accurate queries in graph stream

The graph stream has recently arisen in many interactive scenarios. Characterizing with large volume and high dynamic, graph streams are known to be difficult for high-speed summary and analysis, especially provided with limited resource availability. Existing solutions mainly use sketch-based methods to estimate the weight of items (e.g., Count-Min Sketch) and preserve the underlying graph structure information (e.g., TCM). Unfortunately, these solutions neither support complex graph-based queries nor achieve efficient real-time queries. In view of these limitations, we design DMatrix, a novel 3-dimensional graph sketch to facilitate fast and accurate queries in graph stream. Both structural query and weight-based estimation are supported with DMatrix. Through the integration of representative key reservation and majority voting, DMatrix can effectively narrow the error bounds of queries with real-time response efficiency. Both theoretical analysis and experimental results confirm that our solution is superior in accuracy and efficiency comparing with the state-of-the-art.

Personalized query recommendation using semantic factor model

In the field of query recommendation, the current techniques for semantic analysis technology can't meet the demands of users. In order to meet diverse needs, we improved the LDA model and designed a new query recommendation model based on collaborative filtering-Semantic Factor Model (SFM), which combines text information, user interest information and web source. First, we improved the LDA model from bag-of-word to bag-of-phrase to understand the topics expressed by users' frequently used sentences. The phrase bag model treats phrases as a whole and can capture more accurate query intent. Second, we use collaborative filtering to build an evaluation matrix between user interests and personalized expressions. Third, we designed a new scoring function that can recommend the top n resources to users. Finally, we conduct experiments on the AOL data set. The experimental results show that compared with other latest query recommendation techniques, SFM has higher recommendation quality.

A demonstration of QARTA

This demo presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to: (a) construct its own highly accurate map in terms of both map topology and edge weights, and (b) calibrate its query answers based on contextual information, including transportation modality, underlying algorithm, and time of day/week. The demo is based on actual deployment of QARTA in all Taxis in the State of Qatar and in the third-largest food delivery company in the country, and receiving hundreds of thousands of daily API calls with a real-time response time. Audience will be able to interact with the demo through various scenarios that show QARTA map and query accuracy as well as internals of QARTA.

Data synthesis via differentially private markov random fields

This paper studies the synthesis of high-dimensional datasets with differential privacy (DP). The state-of-the-art solution addresses this problem by first generating a set M of noisy low-dimensional marginals of the input data D , and then use them to approximate the data distribution in D for synthetic data generation. However, it imposes several constraints on M that considerably limits the choices of marginals. This makes it difficult to capture all important correlations among attributes, which in turn degrades the quality of the resulting synthetic data. To address the above deficiency, we propose PrivMRF, a method that (i) also utilizes a set M of low-dimensional marginals for synthesizing high-dimensional data with DP, but (ii) provides a high degree of flexibility in the choices of marginals. The key idea of PrivMRF is to select an appropriate M to construct a Markov random field (MRF) that models the correlations among the attributes in the input data, and then use the MRF for data synthesis. Experimental results on four benchmark datasets show that PrivMRF consistently outperforms the state of the art in terms of the accuracy of counting queries and classification tasks conducted on the synthetic data generated.

Catch a blowfish alive

Policy-aware differential privacy (DP) frameworks such as Blowfish privacy enable more accurate query answers than standard DP. In this work, we build the first policy-aware DP system for interactive data exploration, BlowfishDB, that aims to (i) provide bounded and flexible privacy guarantees to the data curators of sensitive data and (ii) support accurate and efficient data exploration by data analysts. However, the specification and processing of customized privacy policies incur additional performance cost, especially for datasets with a large domain. To address this challenge, we propose dynamic Blowfish privacy which allows for the dynamic generation of smaller privacy policies and their data representations at query time. BlowfishDB ensures same levels of accuracy and privacy as one would get working on the static privacy policy. In this demonstration of BlowfishDB, we show how a data curator can fine-tune privacy policies for a sensitive dataset and how a data analyst can retrieve accuracy-bounded query answers efficiently without being a privacy expert.

Semantic query expansion method based on pay-as-yougo fashion for graph model

The search requirements of users are usually vague and unclear, and the traditional keyword query method is difficult to obtain satisfactory query results. Therefore, the semantic query expansion technology uses semantic information to modify and extend the initial search requirements of users, to obtain more comprehensive and accurate query results. However, the construction cost of the extended query graph is high, and the existing research is difficult to adjust and optimize the user’s query demand in time, resulting in a poor query effect. To solve the above problems, this paper proposes a semantic expansion method based on pay-as-you-go fashion for graph model: Firstly, the initial query graph is constructed according to the user’s search requirements, and the semantic similarities between the search requirements and knowledge bases are calculated; Then, the extended sets of attribute-values and edges are generate by sorting the similarity value in descending order; Finally, all or part of the elements in the extended set are combined to generate the extended query graph. This method can dynamically adjust and optimize the initial query based on the relevant semantic information in time, and improve the query efficiency and effect.

A Differentially private hybrid decomposition algorithm based on quad-tree

Nowadays, spatial data provides users with more convenient services because of its wide application scenarios and rich content. At the same time, the collection and release of spatial data also bring risks of privacy disclosure. In this paper, a differentially private hybrid hierarchical decomposition algorithm named DP-HDAQT is proposed for privacy preserving spatial data query. Data dependent adaptive density grid decomposition is used for coarse sparse/dense region splitting at the first layer. Then for dense regions, an improved quad-tree decomposition way is adopted for further sub-region splitting by introducing bias count to eliminate the impact of recursion depth on a query result. Geometric budget allocation strategy and Laplace noise are used during the decomposition process for privacy preservation. Experimental results demonstrate that the proposed algorithm achieves better accuracy of the count queries in the condition of preserving data privacy well.

Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. Comparing with state-of-the-art methods on widely used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach.

Toward more efficient locality‐sensitive hashing via constructing novel hash function cluster

AbstractLocality‐sensitive hashing (LSH) is widely used in the context of nearest neighbor search of large‐scale high‐dimensions. However, there are serious imbalance problems between the efficiency of data index structure construction and the query accuracy of LSH methods. In this article, a novel higher‐entropy‐hyperplane clusters LSH (HEHC‐LSH) algorithm is proposed, which we improve vector quantization to preprocess the data and greatly shortens the preprocessing time; We innovatively integrate the maximum entropy principle into the distribution estimation algorithm to construct a novel hash function cluster method, also incorporate bootstrap aggregating of ensemble learning, and adopt the parallel index dictionary to improve the generalization performance of the index structure. And in the query stage, we realize the comprehensive filtering of index set using integrated learning idea, which not only avoids a lot of distance calculation, but also improves the quality of query results. We also analyze the rationality and effectiveness of the proposed method. Finally, extensive experiment results show that HEHC‐LSH can achieve more higher precision and efficiency simultaneously comparing to current methods, and reflect the strong robustness on different datasets.

Research on Adaptive Updating Method of Education Resource Index Based on Mobile Computing

When using the current method to update the index of educational resources, there is no analysis of the hierarchical association structure between educational resources. There are some problems such as too long index construction time, high query time and low query accuracy. Therefore, this paper introduces mobile computing to study the adaptive updating method of education resource index. This paper analyzes the structure of the index system of educational resources. On the basis of mobile computing, the hierarchical association structure between educational resources is analyzed by LDA (Latent Dirichlet Allocation) topic modeling and topic hierarchical clustering. The initial query is expanded by selecting extension words by local co-occurrence method. The weight of the extended query is allocated by genetic algorithm to realize the adaptive updating of the index of educational resources. The experimental results show that when the number of nodes is 25 × 104, the total query time of this method is only 3.5 s, and the average index redundancy rate is only 0.22%, indicating that the resource index update performance of this method is better.

Distributed aggregation-based attributed graph summarization for summary-based approximate attributed graph queries

With the drastically increasing size of graph data with more diversified and complex structures, it becomes more challenging to summarize and query large attributed graph data. In this paper, we propose a holistic approach for distributed aggregation-based attributed graph summarization for large-scale approximate attributed graph queries, which incorporates node attributes and relationships into topological structure for generating semantic understandable graph summary in a bottom-up way. First, we propose a holistic strategy of node aggregation to calculate the topological and attributed error increments of merging node pairs. Second, we propose a three-stage distributed implementation framework, where a novel heuristic measure for efficient parallelization is presented to reduce computation and communication costs across multiple machines. Third, a summary-based approximate graph query approach is introduced to accelerate graph query while maintaining high query accuracy. At last, extensive experiments were made over three real-world and synthetic attributed graphs. The results show that our approach has competitive performance in maintaining low error increment and computational costs in comparison with the state-of-the-art aggregation-based graph summarization approach, and that our summary-based approximate graph query can accelerate graph query while maintaining high query accuracy.

A parametric approximation algorithm for spatial group keyword queries

With the application of big data, various queries arise for information retrieval. Spatial group keyword queries aim to find a set of spatial objects that cover the query keywords and minimize a goal function such as the total distance between the objects and the query point. This problem is widely found in database applications and is known to be NP-hard. Efficient algorithms for solving this problem can only provide approximate solutions, and most of these algorithms achieve a fixed approximation ratio (the upper bound of the ratio of an approximate goal value to the optimal goal value). Thus, to obtain a self-adjusting algorithm, we propose an approximation algorithm for achieving a parametric approximation ratio. The algorithm makes a trade-off between the approximation ratio and time consumption enabling the users to assign arbitrary query accuracy. Additionally, it runs in an on-the-fly manner, making it scalable to large-scale applications. The efficiency and scalability of the algorithm were further validated using benchmark datasets.

Efficient and Privacy-Preserving Similarity Range Query Over Encrypted Time Series Data

Similarity query over time series data plays a significant role in various applications, such as signal processing, speech recognition, and disease diagnosis. Meanwhile, driven by the reliable and flexible cloud services, encrypted time series data are often outsourced to the cloud, and as a result, the similarity query over encrypted time series data has recently attracted considerable attention. Nevertheless, existing solutions still have issues in supporting similarity queries over time series data with different lengths, query accuracy and query efficiency. To address these issues, in this article, we propose a new efficient and privacy-preserving similarity range query scheme, where the time warp edit distance (TWED) is used as the similarity metric. Specifically, we first organize time series data into a <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> d-tree by leveraging TWED’s triangle inequality, and design an efficient similarity range query algorithm for the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> d-tree. Second, based on a symmetric homomorphic encryption technique, we carefully devise a suite of privacy-preserving protocols to provide a security guarantee for <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> d-tree based similarity range queries. After that, by using the similarity range query algorithm and these protocols, we propose our privacy-preserving similarity range query scheme, in which we elaborate on two strategies to make our scheme resist against the cloud inference attack. Finally, we analyze the security of our scheme and conduct extensive experiments to evaluate its performance, and the results indicate that our proposed scheme is indeed privacy-preserving and efficient.

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

The heterogeneity of today’s Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user’s information needs by different data modalities, such as keywords, hashtags, user profiles, and other media. Recent IR systems answer such a multi-modal query by considering it as a set of separate uni-modal queries. However, depending on the chosen operationalisation, such an approach is inefficient or ineffective. It either requires multiple passes over the data or leads to inaccuracies since the relations between data modalities are neglected in the relevance assessment. To mitigate these challenges, we present an IR system that has been designed to answer genuine multi-modal queries. It relies on a heterogeneous network embedding, so that features from diverse modalities can be incorporated when representing both, a query and the data over which it shall be evaluated. By embedding a query and the data in the same vector space, the relations across modalities are made explicit and exploited for more accurate query evaluation. At the same time, multi-modal queries are answered with a single pass over the data. An experimental evaluation using diverse real-world and synthetic datasets illustrates that our approach returns twice the amount of relevant information compared to baseline techniques, while scaling to large multi-modal databases.

Usefulness of web search queries for early detection of diseases in infants.

ABSTRACTEarly detection of diseases is critical in infants. This study evaluates the usefulness of web searches in predicting diseases in order to encourage guardians to consult a doctor promptly if their children are ill. We collected six months of search queries from Yahoo! JAPAN Search between October 2016 and March 2017. Using a machine learning model, we investigated the accuracy of the search query’s ability to predict the diagnosis of biliary atresia and hypertrophic pyloric stenosis. Both diseases were modeled with an accuracy of approximately 80%, and symptoms related to the disease were significant features in the model. These findings suggest the possibility of detecting diseases from web search queries performed by guardians. Through future research, we intend to propose a method that uses web search queries for early detection of these diseases by providing appropriate and timely information to support the guardians of patients.

Reinforcement-Learning-Based Query Optimization in Differentially Private IoT Data Publishing

With the advancement of Internet of Things (IoT) and computing paradigms, massive data are collected and processed to enhance intelligent applications. However, by deliberately sending some queries, an attacker may be able to derive the sensitive information of IoT data owners. To prevent privacy leakage during IoT data query, differential privacy (DP) hides private information by introducing noise to the query results. As DP introduces randomized noise that will affect query accuracy (data utility), the tradeoff between privacy preservation and data utility is a challenge. In this article, we first propose a novel optimization framework for single query to minimize the privacy cost, while satisfying both personalized DP and customized data utility. We design a reinforcement learning-based algorithm for single query optimization framework (SQOF_RL) to solve the optimization problem efficiently. Then, we propose a SQOF_RL and SVT-based batch query optimization mechanism (S2BQOM) to answer more queries privately. The performance evaluation shows that SQOF_RL and S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BQOM can effectively optimize single query and batch queries in terms of privacy cost, data utility, personalized privacy, and query satisfaction. Finally, the performance analysis reveals that our work can be applied to multiple linear/nonlinear query functions instead of one particular query function.