Query Method Research Articles

A secure and efficient range query method for two‐layer wireless sensor networks

Traditional range query methods of work still have shortcomings in node energy consumption and privacy security, so a two-layer secure and efficient range query method for wireless sensor networks is proposed. In the data storage stage, the sensing node obtains the data ciphertext and timestamp by the Advanced Encryption Standard encryption algorithm, receives the new encryption constraint chain by the reverse 0–1 encoding method and Hash-based Message Authentication Code encryption algorithm, and sends the chain to the storage node. In the query response phase, the storage node responds to the request of the base station and sends the data that meet the query requirements. After receiving the data, the base station verifies the consistency with the new encryption constraint chain and timestamp. During the experiment, the energy consumption is analysed from three aspects: the number of data collected in the period, the data length of the sensing node and the partition factor of the encryption constraint chain. The results show that this method has low energy consumption and can maintain the consistency of data.

A Reconstruction Attack Scheme on Secure Outsourced Spatial Dataset in Vehicular Ad-Hoc Networks

In the cloud-based vehicular ad-hoc network (VANET), massive vehicle information is stored on the cloud, and a large amount of data query, calculation, monitoring, and management are carried out at all times. The secure spatial query methods in VANET allow authorized users to convert the original spatial query to encrypted spatial query, which is called query token and will be processed in ciphertext mode by the service provider. Thus, the service provider learns which encrypted records are returned as the result of a query, which is defined as the access pattern. Since only the correct query results that match the query tokens are returned, the service provider can observe which encrypted data are accessed and returned to the client when a query is launched clearly, and it leads to the leakage of data access pattern. In this paper, a reconstruction attack scheme is proposed, which utilizes the access patterns in the secure query processes, and then it reconstructs the index of outsourced spatial data that are collected from the vehicles. The proposed scheme proves the security threats in the VANET. Extensive experiments on real-world datasets demonstrate that our attack scheme can achieve quite a high reconstruction rate.

English Audio Language Retrieval Based on Adaptive Speech-Adjusting Algorithm

For the purpose of language retrieval for English listening, this paper designs and implements a cross-language information retrieval system for English listening. Different implementation methods of cross-language information retrieval, query, and translation are analyzed. The system adopts cross-language information retrieval technology based on bilingual dictionaries. According to the cross-language retrieval system of the existing bilingual dictionaries and monolingual dictionaries, based on the design and implementation of the fuzzy search dictionary lookup mechanism, the existing dictionary lookup mechanism is constructed and analyzed. Aiming at the problem of translation ambiguity in information retrieval systems based on bilingual dictionaries, a disambiguities elimination algorithm based on cooccurrence technology is proposed. In continuous speech, the speed of different speakers in different contexts is very different. Deviation from normal speech speed often leads to recognition errors, which makes recognition performance decline. Considering that the influence of speech speed on the length of speech units increases or decreases synchronously, and there is a strong correlation between the lengths of adjacent speech units, an adaptive speech speed algorithm is proposed based on the framework of implicit Markov model based on the information of the length of speech units. Experiments on number string and large vocabulary continuous speech recognition show that the algorithm is effective.

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 Practical Index Structure Supporting Fréchet Proximity Queries among Trajectories

We present a scalable approach for range and k nearest neighbor queries under computationally expensive metrics, like the continuous Fréchet distance on trajectory data. Based on clustering for metric indexes, we obtain a dynamic tree structure whose size is linear in the number of trajectories, regardless of the trajectory’s individual sizes or the spatial dimension, which allows one to exploit low “intrinsic dimensionality” of datasets for effective search space pruning. Since the distance computation is expensive, generic metric indexing methods are rendered impractical. We present strategies that (i) improve on known upper and lower bound computations, (ii) build cluster trees without any or very few distance calls, and (iii) search using bounds for metric pruning, interval orderings for reduction, and randomized pivoting for reporting the final results. We analyze the efficiency and effectiveness of our methods with extensive experiments on diverse synthetic and real-world datasets. The results show improvement over state-of-the-art methods for exact queries, and even further speedups are achieved for queries that may return approximate results. Surprisingly, the majority of exact nearest-neighbor queries on real datasets are answered without any distance computations.

Rail transport as an element of the national defense security

One of the main areas of national security is defense security. Its provision requires many various activities of a political and military nature. An important role is played by the ability to move forces and resources at the army's disposal within the territory of the country and outside it by various modes of transport. From the perspective of current conditions, the expansion of transport networks of all modes of transportation is necessary to ensure the required conditions for the movement of people and the transport of armaments and military equipment, meet the living needs of the population and the possibility of its evacuation, support the Polish Armed Forces and allied forces in the event of a crisis or war. A modern railway network enables the development of the Polish economy and strengthens its link with the world economy. It is also an essential component of national security, the national defense system, and the territorially balanced development of the country. Modern transport infrastructure, modernized rolling stock, and integrated transport management systems reduce costs in the economy while lowering the pressure on the environment. Systematic improvement of transport infrastructure is one of the primary challenges for the nation's development, not only from the economic perspective but also plays a priority role concerning national defense security. The aim of the article is to identify the essence and importance of rail transport for ensuring national defense security. The research problem undertaken has been specified in the following form: To what extent does the development of rail transport affect the national defense security, and what actions should be taken to improve military mobility in rail transport? The material uses several research methods, the leading of which were: literature query, analysis, and synthesis, abstraction, and inference methods. The adopted approach made it possible to obtain a cross-sectional character of the considerations while providing the basis for further, extended research on this highly complex problem.

Pipelined XPath Query Based on Cost Optimization

XPath query is the key part of XML data processing, and its performance is usually critical for XML applications. In the process of XPath query, there is inherent seriality between query steps, which makes it difficult to parallelize the query effectively as a whole. On the other hand, although XPath query has the characteristics of data stream processing and is suitable for pipeline processing, the data flow of each query step usually varies a lot, which results in limited performance under multithreading conditions. In this paper, we propose a pipelined XPath query method (PXQ) based on cost optimization. This method uses pipelined query primitives to process query steps based on relation index. During pipeline construction, a cost estimation model based on XML statistics is proposed to estimate the cost of the query primitive and provide guidance for the creation of a pipeline phase through the partition of query primitive sequence. The pipeline construction technique makes full use of available worker threads and optimizes the load balance between pipeline stages. The experimental results show that our method can adapt to the multithreaded environment and stream processing scenarios of XPath query, and its performance is better than the existing typical query methods based on data parallelism.

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.

LitSuggest: a web-based system for literature recommendation and curation using machine learning.

Searching and reading relevant literature is a routine practice in biomedical research. However, it is challenging for a user to design optimal search queries using all the keywords related to a given topic. As such, existing search systems such as PubMed often return suboptimal results. Several computational methods have been proposed as an effective alternative to keyword-based query methods for literature recommendation. However, those methods require specialized knowledge in machine learning and natural language processing, which can make them difficult for biologists to utilize. In this paper, we propose LitSuggest, a web server that provides an all-in-one literature recommendation and curation service to help biomedical researchers stay up to date with scientific literature. LitSuggest combines advanced machine learning techniques for suggesting relevant PubMed articles with high accuracy. In addition to innovative text-processing methods, LitSuggest offers multiple advantages over existing tools. First, LitSuggest allows users to curate, organize, and download classification results in a single interface. Second, users can easily fine-tune LitSuggest results by updating the training corpus. Third, results can be readily shared, enabling collaborative analysis and curation of scientific literature. Finally, LitSuggest provides an automated personalized weekly digest of newly published articles for each user's project. LitSuggest is publicly available at https://www.ncbi.nlm.nih.gov/research/litsuggest.

Dietary Structure and Nutritional Status of Chinese Beekeepers: Demographic Health Survey.

BackgroundBeekeeping and honey gathering are traditional forms of agricultural farming in China. However, only few studies have focused on the nutritional status and health level of this special occupational group.ObjectiveBy comparing the health status of apiculturists (beekeepers) and vegetable farmers in plain areas of Hubei Province, and analyzing the influence of dietary structure and intake on their nutritional level, this paper provides a scientific theoretical basis for the further development of health education and disease prevention for beekeepers.MethodsFrom February to April 2016, 191/236 beekeepers (80.9% of the total beekeepers) with large-scale breeding (300-500 colonies) and 182 vegetable farmers in the same area were sampled by the cluster sampling method. Their nutrient composition was analyzed using a human body composition analyzer, dietary structure information was collected using the dietary frequency query method, and cognitive function was investigated. In addition, blood samples of both groups were collected.ResultsA total of 362 valid questionnaires (beekeepers/vegetable farmers: 185/177) were collected, with an effective response rate of 97.1% (362/373). Both beekeepers and vegetable farmers were overweight, and the beekeepers’ grip strength was much stronger than that of the vegetable farmers’ regardless of gender. The dietary structure of beekeepers is very unique: 29.7% (55/185) of beekeepers indicated consuming royal jelly regularly for more than 10 years. Their main foods are grain, cereals, and fresh vegetables; 68.1% (126/185) of the beekeepers never drank milk and other dairy products, and their overall nutrient intake is unbalanced. The average intake of cellulose in this group was also significantly higher than that in the epidemiological survey in the same sex and age group. The intake of vitamin A and selenium in the beekeepers group was significantly higher than that in the vegetable-farmers group (all P<.001). The blood indices of creatinine (P=.03) and blood copper (P<.001) in the beekeepers group were significantly higher than those in the vegetable-farmers group, and the total protein, albumin, calcium, sodium, potassium, phosphorus, folic acid, and vitamin B12 in the beekeepers group were significantly lower than those in the vegetable-farmers group (P<.03 for potassium and P<.001 for others). The total Mini-Mental State Examination (MMSE) score of the beekeepers group was 28.1, significantly higher (P=.006) than that of the vegetable-farmers group (23.3).ConclusionsThe beekeepers in this area have their special dietary structure, body nutrient level, and disease characteristics. The cognitive level of the beekeepers who regularly consume royal jelly is significantly higher than that of their peers. The chronic diseases of this special occupational group are closely related to their lifestyle and nutritional status, so more attention and in-depth studies are needed to improve the quality of life of this population.

Point cloud completion using multiscale feature fusion and cross-regional attention

Raw point clouds obtained from real-world scanning are always incomplete and ununiformly distributed, which would result in structural losses of object shapes and bring about difficulties in further high-level 3D vision tasks. Therefore, a learning-based method called CRA-Net is proposed in this paper to repair partial point clouds and predict complete object shapes. Compared with most existing networks that only leverage global features, CRA-Net successfully utilizes local features to restore clearer details of object shapes with low instability. First, we propose an adaptive neighborhood query method that is able to adjust query centers and radiuses to cover different object shapes and acquire balanced local regions. Second, we build a parallel encoder to extract multiscale features from the input. Third, we design a cross-regional attention module based on graph attention network. It quantifies underlying relationships among all the local features under certain conditions interpreted by global features. Based on such relationships, each conditional local feature vector is able to search across the regions and selectively absorb other local features. Fourth, we design a coarse decoder to collect these cross region features and generate the skeleton of complete point cloud. Finally, we refine the coarse point cloud by comparing it with the input, and up sample it using folding-based layers.Our network is first trained and tested on manually made partial-complete point clouds pairs generated by the scanning process of a virtual LiDAR on eight categories of objects. Then it is tested on real-world point clouds of indoor and outdoor scenes. Compared with existing representative methods, our CRA-Net always restores the most accurate point clouds with the clearest details.

Innovation Strategy of Film and Television Creation under the Background of Big Data

With the advent of the information age, the role of knowledge resources in modern social production is expanding. In today’s era, people have mastered a variety of information storage, processing and query methods, thus greatly expanding the scope of automation. At the same time, people have higher and higher requirements for the film and television drama, not only for the rich content and plot, but also for the actor’s acting skills. Through big data, we can analyze the types of movies popular with the public, so that the market can create films and TV programs according to their favorite degree of movies. Based on this, this paper analyzes the current situation of China’s film and television drama, combined with the background of the era of big data, studies the innovation of film and television creation. In this paper, a questionnaire survey is conducted to analyze the 10 most popular Chinese movies on Douban in 2019. The experimental results show that the public agree with the film quality in 2019. At the same time, among the favorite film types, the public’s love for inspirational movies and TV plays was the highest, reaching 86%, followed by social ethics drama with 84%, and youth idol drama with the lowest love rate of only 52%.

Application of processing technology based on skyline query in computer network

With the rapid development of network data communication technology, the complexity of the network environment makes the data in the data stream have uncertain characteristics, and a large number of data streams are generated in many fields. Therefore, it is necessary to find an efficient and accurate processing method to process a large amount of data in a computer network. This paper takes the skyline probability calculation and the actual efficiency of the unknown object index structure in the skyline query processing technology on the uncertain data stream as the research object. Based on the skyline query technology, we study an efficient skyline query processing method on the unknown network traffic based on the model SUMG. The method includes two algorithms: dynamic modeling algorithm DMG and skyline query algorithm GST. The DMG algorithm samples the data in the sliding window of the uncertain data stream and establishes a model to convert the data stream into the parameter stream in the uncertain object probability density function, the GST algorithm establishes the R-tree index structure, which is in order to reduce the amount of calculation, it uses the parameter flow of the model. In both methods, the set of local skyline results is first obtained at the distributed node, and the skyline query is performed again on the union of the local skyline results to obtain the global skyline result set. The experimental results show that compared with the skyline query method BNL for unknown network traffic without an index structure, the SUMG method can not only effectively model the link-type unknown object to assist the skyline query, but also effectively prune the uncertain data object and improve the skyline. The distributed skyline query method can cope with the skyline query task on the distributed data stream.

SPEAR: Dynamic Spatio-Temporal Query Processing over High Velocity Data Streams.

With the advent of IoT and emerging 5G technology, real-time streaming data are being generated at unprecedented speed and volume, and coming with both temporal and spatial dimensions. Effective analysis at such scale and speed requires support for dynamically adjusting querying capabilities in real-time. In spatio-temporal domain, this warrants for data as well as query optimization strategies especially for objects with changing motion states. Contemporary spatio-temporal data stream management systems in distributed domain are mostly dominated by specified-once-applied-continuously query model. Any modification in query state requires query restart limiting system responsiveness and producing outdated or in worst case erroneous results. In this paper, we propose adaptations of principles from streaming databases, spatial data management and distributed computing to support dynamic spatio-temporal query processing over high velocity big data streams. We first formulate a set of spatio-temporal data types and functions to seamlessly handle changes in distributed query states. We develop a comprehensive set of streaming spatio-temporal querying methods, and propose geohash based dynamic spatial partitioning for effective parallel processing. We implement a prototype on top of Apache Flink, where the in-memory stream processing fits nicely with our spatio-temporal models. Comparative evaluation of our prototype demonstrates the effectiveness our strategy by maintaining high consistent processing rates for both stationary as well as moving queries over high velocity spatio-temporal big data streams.

Query the trajectory based on the precise track: a Bloom filter-based approach.

Fast and precise querying in a given set of trajectory points is an important issue of trajectory query. Typically, there are massive trajectory data in the database, yet the query sets only have a few points, which is a challenge for the superior performance of trajectory querying. The current trajectory query methods commonly use the tree-based index structure and the signature-based method to classify, simplify, and filter the trajectory to improve the performance. However, the unstructured essence and the spatiotemporal heterogeneity of the trajectory-sequence lead these methods to a high degree of spatial overlap, frequent I/O, and high memory occupation. Thus, they are not suitable for the time-critical tasks of trajectory big data. In this paper, a query method of trajectory is developed on the Bloom Filter. Based on the gridded space and geocoding, the spatial trajectory sequences (tracks) query is transformed into the query of the text string. The geospace was regularly divided by the geographic grid, and each cell was assigned an independent geocode, converting the high-dimensional irregular space trajectory query into a one-dimensional string query. The point in each cell is regarded as a signature, which forms a mapping to the bit-array of the Bloom Filter. This conversion effectively eliminates the high degree of overlap and instability of query performance. Meanwhile, the independent coding ensures the uniqueness of the whole tracks. In this method, there is no need for additional I/O on the raw trajectory data when the track is queried. Compared to the original data, the memory occupied by this method is negligible. Based on Beijing Taxi and Shenzhen bus trajectory data, an experiment using this method was constructed, and random queries under a variety of conditions boundaries were constructed. The results verified that the performance and stability of our method, compared to R*tree index, have been improved by 2000 to 4000 times, based on one million to tens of millions of trajectory data. And the Bloom Filter-based query method is hardly affected by grid size, original data size, and length of tracks. With such a time advantage, our method is suitable for time-critical spatial computation tasks, such as anti-terrorism, public safety, epidemic prevention, and control, etc.

An efficient index method for the optimal path query over multi-cost networks

In the past couple of decades, graphs have been widely used to model complex relationships among various entities in real applications. Shortest path query is a fundamental problem in graphs and has been well-studied. Most existing approaches for the shortest path problem consider that there is only one kind of cost in networks. However, there always are several kinds of cost in networks and users prefer to select an optimal path under the global consideration of these kinds of cost. In this paper, we study the problem of finding the optimal path in the multi-cost networks. We prove this problem is NP-hard and the existing index techniques cannot be used to this problem. We propose a novel partition-based index with contour skyline techniques to find the optimal path. We propose a vertex-filtering algorithm to facilitate the query processing. We conduct extensive experiments on six real-life networks and the experimental results show that our method has an improvement in efficiency by an order of magnitude compared to the previous heuristic algorithms.

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.

Querying multi-source heterogeneous fuzzy spatiotemporal data

With the rapid development of the environmental, meteorological and marine data management, fuzzy spatiotemporal data has received considerable attention. Even though some achievements in querying aspect have been made, there are still some unsolved problems. Semantic and structural heterogeneity may exist among different data sources, which will lead to incomplete results. In addition, there are ambiguous query intentions and conditions when the user queries the data. This paper proposes a fuzzy spatiotemporal data semantic model. Based on this model, the RDF local semantic models are converted into a RDF global semantic model after mapping relational data and XML data to RDF local semantic models. The existing methods mainly convert relational data to RDF Schema directly. But our approach converts relational data to XML Schema and then converts it to RDF, which utilizes the semi-structured feature of XML schema to solve the structural heterogeneity between different data sources. The integration process enables us to perform global queries against different data sources. In the proposed query algorithms, the query conditions inputted are converted into exact queries before the results are returned. Finally, this paper has carried out extensive experiments, calculated the recall, precision and F-Score of the experimental results, and compared with other state-of-the-art query methods. It shows the importance of the data integration method and the effectiveness of the query method proposed in this paper.

Research on global tourism information query method based on association mining

The traditional global tourism information query method cannot mine the internal relevance of discrete information, which leads to the problem of inaccurate query results and long query response time. This paper proposes a method of global tourism information query based on association mining. Using SAS/ME tools, the data of global tourism information is processed and screened. According to the results of data processing, the whole tourism information dataset is constructed. The C4.5 decision tree algorithm and SAS miner enterprise are used to mine the association of tourism information in the whole region. According to the results of association mining, a global tourism information query system is established to realise the global tourism information query. The experimental results show that this method has the shortest response time and higher query precision, which shows that this method is more suitable for the global tourism information query.

Research on global tourism information query method based on association mining

The traditional global tourism information query method cannot mine the internal relevance of discrete information, which leads to the problem of inaccurate query results and long query response time. This paper proposes a method of global tourism information query based on association mining. Using SAS/ME tools, the data of global tourism information is processed and screened. According to the results of data processing, the whole tourism information dataset is constructed. The C4.5 decision tree algorithm and SAS miner enterprise are used to mine the association of tourism information in the whole region. According to the results of association mining, a global tourism information query system is established to realise the global tourism information query. The experimental results show that this method has the shortest response time and higher query precision, which shows that this method is more suitable for the global tourism information query.