Data Retrieval Problem Research Articles

A Retrieval Algorithm for Time-constrained Heterogeneous Data Items in Wireless Networks

<p>Homogeneous and heterogeneous data retrieval problem refers that these requested data items are retrieved on the basis of data items broadcast at some channels such that the access latency of all requested data items is minimized. The problem has an important study significance in MIMO (Multiple Input and Multiple Output) wireless communication. Almost researches are focused on proposing some schemes for solving signal antenna or multiple antennae data retrieval based on homogeneous data items, but these schemes are not suitable for heterogeneous data retrieval problem. So in this paper, we describe a SPR (Shortest Paths Retrieval) algorithm that is suitable for solving homogeneous and heterogeneous data retrieval problem at the same time. Moreover, for avoiding the retrieval time too long, when the each requested data item is set a constrained condition-deadline, we propose another DKPR (Deadline-based Key Path Retrieval) algorithm that finds the critical path with the largest number of requested data items in a broadcast cycle, so as to complete the retrieval operation of data items within the deadline of requesting data items as much as possible. Through experiments, the proposed scheme has good efficiency. </p> <p> </p>

MM-FOOD: a high-dimensional index structure for efficiently querying content and concept of multimedia data

The semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structure’s construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large.

Supervision Of Bonded Area Facility Policies For Importers At The Bekasi Customs And Excise Intermediate Type A Supervision And Service Office For 2018-2019

One particular phenomenon in this research is that the Bonded Zone facility policy has its own appeal in the producers' eyes on an export basis. Consequently, judging from the large number of Bonded Zone scattered throughout Bekasi City, it becomes a significant obstacle for the supervisory duty’s implementation. The number of personnel within the Directorate General of Customs and Excise (DJBC) is inadequate, which in turn, causes the practice of misusing DJBC's superior facilities is easier to do. This study aims to determine and analyze the Bonded Zone facility policy supervision for importers at the supervision and Service Office for Customs and Excise in Type A of Bekasi Customs and Excise and the constrictions and efforts in the supervision process. The research method employed was descriptive research with a qualitative approach. Meanwhile, the data analysis method implemented was qualitative data analysis. The results of this study indicated that the policy supervision of Bonded Zone facility for importers at KPPBC TMP A Bekasi has been going well on each year. However, it has not been optimally implemented. It is indicated by the findings from the inspection results and action carried out by KPPBC TMP A Bekasi. In implementing the bonded area facility policy supervision, KPPBC TMP A Bekasi tackled various obstacles, nearly data retrieval problems on the CEISA TPB application, the unmatched company IT Inventory to what was reported, and the insufficient number of personnel in the supervisory unit.

Developing Data-driven Artificial Neural Network for a High Throughput Retrieval of Aerosol Optical Depth and Surface Temperature of Mars

In this work, we aim to develop artificial neural network (ANN) techniques to reproduce the retrieval results of physical quantities from spacecraft observations of solar system bodies using radiative transfer methods. The particular application here is the retrieval of dust optical depth, water ice optical depth, and surface temperature on Mars using daytime observations obtained by the Thermal Emission Spectrometer on board the Mars Global Surveyor. Compared against the results obtained from traditional radiative transfer retrieval techniques, our ANN successfully recovered the three quantities using daytime observations. The principal advantage of these machine-learning algorithms is their complete automation and high throughput. Therefore, the algorithms presented here would be useful for very large data sets and would make practical the sampling of many different approximations or boundary conditions related to a given observation data set and retrieval problem.

Big Data Backup Deduplication : A Survey

The massive explosion in the field of data such as images, video, audio, and text has caused significant problems in data storage and retrieval. Companies and organizations spend a lot of money to store and manage data. Therefore, there is an urgent need for efficient technologies to deal with this massive amount of data. One of the essential techniques to eliminate redundant data is data deduplication and data reduction. The best technique used for this purpose is data deduplication. Data deduplication decreases bandwidth, hard disc drive utilization, and backup costs by removing redundant data. This paper focuses on studying the literature of several research papers related to data deduplication for various techniques that several researchers have proposed. It summarized multiple concepts and techniques related to deduplication and methods used to improve storage. The data deduplication processes were examined in detail, including data chunking, hashing, indexing, and writing. Also, this study discussed the most critical problems faced by the data deduplication algorithm.

Information Retrieval in Text-Based Document using Boyer Moore Algorithm

The Language Development Center (LDC) is a language laboratory that annually serves more than 1000 participants in activities such as standardized tests and language courses for students and lecturers. This activity generates a lot of data which is still managed ineffectively and causes several problems in data retrieval, archiving, and monitoring. Therefore, this research was made with the aim of creating a system so that data management in LDC becomes more effective. In designing and building this system, the writers uses the Waterfall method and also implements the Boyer Moore algorithm in the data search process. Boyer moore algorithm helps the data search process more effectively and can search for words in several text-based files at once. The result of this research is a web-based data management information system that makes the data management process becomes more effective and efficient.
 Keywords: information system, data management, data retrieval, Boyer Moore algorithm

A penalty function semi-continuous thresholding methods for constraints of hashing problems

The Hashing process is an effective tool for handling large-scale data (for example, images, videos, or multi-model data) retrieval problems. To get better retrieval accuracy, hashing models usually are imposed with three rigorous constraints, i.e., discrete binary constraint, uncorrelated condition, and the balanced constraint, which will lead to being ‘NP-hard’. In this study, we divide the whole constraints set into the uncorrelated (orthogonality) constraint and the binary discrete balance constraint and propose a fast and accurate penalty function semi-continuous thresholding (PFSCT) hash coding algorithm based on forward–backward algorithms. In addition, we theoretically analyze the equivalence between the relaxed model and the original problems. Extensive numerical experiments on diverse large-scale benchmark datasets demonstrate comparable performance and effectiveness of the proposed method.

EG-SNIK: A Free Viewing Egocentric Gaze Dataset and Its Applications

Egocentric vision data captures the first person perspective of a visual stimulus and helps study the gaze behavior in more natural contexts. In this work, we propose a new dataset collected in a free viewing style with an end-to-end data processing pipeline. A group of 25 participants provided their gaze information wearing Tobii Pro Glasses 2 set up at a museum. The gaze stream is post-processed for handling missing or incoherent information. The corresponding video stream is clipped into 20 videos corresponding to 20 museum exhibits and compensated for user’s unwanted head movements. Then, I-VT algorithm is used to identify eye movements as fixations and saccades. Based on the velocity of directional shifts of the eye, the I-VT algorithm classifies the eye movements into either fixations or saccades. Representative scanpaths are built by generalizing multiple viewers’ gazing styles for all exhibits. The application of our dataset is demonstrated for characterizing the inherent gaze dynamics using state trajectory estimator based on ancestor sampling (STEAS) model in solving gaze data classification and retrieval problems.

Grover algorithm-based quantum homomorphic encryption ciphertext retrieval scheme in quantum cloud computing

Existing classical ciphertext retrieval schemes are mainly developed according to the homomorphic encryption that is a cryptographic technique based on computational complexity theory of mathematical puzzles. In quantum computing, on the one hand, most of traditional asymmetric encryption can be quickly cracked as the computational capacity of quantum computer is much higher than that of traditional digital computer. Hence, the quantum homomorphic encryption scheme is widely used in data encryption for the issue of privacy protection in quantum computing. But on the other the retrieval efficiency of homomorphic encrypted data is insufficient especially in quantum cloud computing. Therefore, this paper proposes a novel quantum homomorphic encryption ciphertext retrieval (QHECR) scheme basing on the Grover algorithm to solve the problem of homomorphic encrypted ciphertext data retrieval in quantum cloud computing. Our scheme is to improve such retrieval efficiency mentioned above where the interaction process is not required even if the T-gate exists in the circuit. In the experiment, two qubits without the T-gate are conducted on both simulations and real quantum devices by using IBM quantum information science kit (Qiskit). The results show that the proposed QHECR scheme is capable of achieving the retrieval encrypted data when the T-gate does not exist in the evaluation circuit in ciphertext environment. Moreover, a verification experiment about the T-gate key update algorithm is implemented to verify the feasibility and reliability of the proposed scheme in the Qiskit as well, which indicates that the QHECR is still available when the T-gate exists in the circuit. Since the decryption is inefficient when there are exponential T-gates in the circuit, our proposed scheme is suitable for low T-gate complexity.

Data grouping scheme for multi-request retrieval in MIMO wireless communication

Multi-antennae data retrieval problem refers that finds an access pattern (to retrieve multiple requests by using multiple antennae, where each request has multiple data items) such that the access latency of some requests retrieved by each antenna is minimised and the total access latency of all requests retrieved by all antennae keeps balance. So it is very important that these requests are divided into multiple groups for achieving the retrieval by using each antenna in MIMO wireless communication, called data grouping problem. There are few studies focused on data grouping scheme applied in data retrieval problem when the clients equipped with multi-antennae send multiple requests. Therefore, this paper proposes two data grouping algorithms (HOG and HEG) that are applied in data retrieval such that the requests can be reasonably classified into multiple groups. Through experiments, the proposed schemes have better efficiency compared with some existing schemes.

DESSE: A Dynamic Efficient Forward Searchable Encryption Scheme

Under the cloud computing environment, Searchable Symmetric Encryption (SSE) is an effective method to solve the problem of encrypted data retrieval, and helps to protect the users' privacy. Recent researches show that some attacks may bring great security threats to SSE, and forward privacy can effectively prevent these attacks, so forward privacy is very necessary for SSE scheme. Most of the existing forward privacy SSE schemes fall into two types: ORAM-based and Bost-based. The former is simple, but it has large communication overhead and low dynamic update efficiency. The latter is better than the former, but it is based on asymmetric encryption primitives. Based on symmetric encryption primitives, we propose a dynamic efficient forward privacy scheme DESSE in this paper. DESSE uses pseudo-random permutation to realize forward privacy, and uses delete list to identify the final state of the same file added and deleted repeatedly, so as to realize the dynamic update of data. The method proposed in this paper is simple and flexible in structure, takes up less additional space, and can significantly improve the efficiency of updating. At the same time, it can achieve real-time updating of data. The correctness of our proposed scheme is tested using the Enron email data set in the end.

Privacy-Preserving and Pairing-Free Multirecipient Certificateless Encryption With Keyword Search for Cloud-Assisted IIoT

Nowadays, cloud-assisted Industrial Internet of Things (IIoT) has become pervasive in modern enterprises, because it supplies a promising way to transform the operation mode of existing industrial facilities, to enhancing the production efficiency and lowering the manufacturing cost. In order to preserve the privacy of enterprises, sensitive industrial data needs to be encrypted prior to being uploaded to the cloud. Recently, certificateless encryption with keyword search (CLKS) was introduced to resolve the problem of encrypted data retrieval in cloud-assisted IIoT. However, the existing CLKS schemes only support a single-recipient keyword search and need to depend on the costly bilinear pairing that is disliked by the resource-constrained IIoT devices. Moreover, most of the existing CLKS schemes are vulnerable to the keyword guessing attack, and thus fail to protect the privacy of searched data. In this article, we develop a privacy-preserving and pairing-free multirecipient CLKS scheme for cloud-assisted IIoT. The proposed scheme has the following merits: 1) supporting multirecipient keyword search function; 2) requiring no costly bilinear pairing operations; and 3) providing resistance against keyword guessing attacks. The performance comparison and analysis demonstrate that it is more efficient than the existing CLKS schemes and is appropriate for the cloud-assisted IIoT.

Real-Time Data Retrieval in Cyber-Physical Systems with Temporal Validity and Data Availability Constraints

Maintaining the temporal validity of real-time data in cyber-physical systems is of critical importance to ensure the correct decision making and appropriate system operation. Most existing work on real-time data retrieval assume that the real-time data under study are always available for retrieval, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity constraints. This assumption, however does not hold in many real-time applications with intermittent data availability. In this paper, we study the Availability-constrained Fresh Data Retrieval (AFDR) problem, which aims to retrieve all required real-time data for a given set of decision tasks on time while taking both the temporal validity and data availability constraints into consideration. We formulate the AFDR problem as an ILP problem and study its complexity under different settings. Given the general case of the AFDR problem is proved to be NP-hard, we focus on the cases that data items have unit-size retrieval time. For the single decision task scenario, we propose a polynomial-time optimal data retrieval algorithm, which consists of a task finish time selection phase and an optimal retrieval schedule construction phase, to solve the AFDR problem. For the multiple decision task scenario, we propose an efficient heuristic algorithm by transforming the temporal validity constraint of a real-time data item to the availability constraint. The effectiveness of the proposed algorithms has been validated through extensive experiments. Our results show that the heuristic algorithm outputs around $1.5\times$1.5× feasible cases compared to that of the state-of-the-art scheme.

Video Big Data Retrieval Over Media Cloud: A Context-Aware Online Learning Approach

Online video sharing (e.g., via YouTube or YouKu) has emerged as one of the most important services in the current Internet, where billions of videos on the cloud are awaiting exploration. Hence, a personalized video retrieval system is needed to help users find interesting videos from big data content. Two of the main challenges are to process the increasing amount of video big data and resolve the accompanying “cold start” issue efficiently. Another challenge is to satisfy the users’ need for personalized retrieval results, of which the accuracy is unknown. In this paper, we formulate the personalized video big data retrieval problem as an interaction between the user and the system via a stochastic process, not just a similarity matching, accuracy (feedback) model of the retrieval; introduce users’ real-time context into the retrieval system; and propose a general framework for this problem. By using a novel contextual multiarmed bandit-based algorithm to balance the accuracy and efficiency, we propose a context-based online big-data-oriented personalized video retrieval system. This system can support datasets that are dynamically increasing in size and has the property of cross-modal retrieval. Our approach provides accurate retrieval results with sublinear regret and linear storage complexity and significantly improves the learning speed. Furthermore, by learning for a cluster of similar contexts simultaneously, we can realize sublinear storage complexity with the same regret but slightly poorer performance on the “cold start” issue compared to the previous approach. We validate our theoretical results experimentally on a tremendously large dataset; the results demonstrate that the proposed algorithms outperform existing bandit-based online learning methods in terms of accuracy and efficiency and the adaptation from the bandit framework offers additional benefits.

Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China

Several studies have attempted to predict ground PM2.5 concentrations using satellite aerosol optical depth (AOD) retrieval. However, over 70%–90% of aerosol retrievals are non-random missing, which limits and biases the estimation. To the best of our knowledge, this issue has not been well resolved to date.The aim of this study was to develop an interpolation technique to handle the missing data retrieval problem and to estimate the daily PM2.5 for a high coverage dataset with 3-km resolution in China by fitting the complex temporal and spatial variations.We developed a two-step interpolation method (i.e., the mixed-effect model and inverse distance weighting technology) to replace the missing values in AOD. Next, the extreme gradient boosting (XGBoost) technique that includes a non-linear exposure-lag-response model (NELRM) was proposed and validated to estimate the daily levels of PM2.5 across China during 2014–2015.After two steps of interpolation, the missing value rate of daily AOD data was reduced from 87.91% to 13.83%. The cross-validation (CV) R-square, root mean square error (RMSE) and mean absolute percentage prediction error (MAPE) of the interpolation were 0.76, 0.10 and 21.41%, respectively. The cross-validation for the prediction of daily PM2.5 resulted in R2 = 0.86, RMSE = 14.98, and MAPE = 23.72%.The results of this study indicate that the two-step interpolation method can largely resolve the non-random missing data problem and that the combined XGBoost methods have a good ability to estimate fine particulate matter concentrations.

Efficient algorithm for online data retrieval with one antenna in wireless networks

Given a set of requested data items and a set of multiple channels, data retrieval problem is to find a data retrieval sequence for downloading all requested data items from these channels in a reasonable time range. Almost all existing schemes are applied in offline environment that the client can learn prior knowledge of wireless data broadcast, such as the set of broadcast channels and the broadcast time of data items. However, this information is unknown in online data retrieval. So this paper proposes three online algorithms (MRLR, RM and MRM) for solving online data retrieval problem. Compared with the optimal offline data retrieval algorithm, MRLR, RM and MRM algorithms hold (k ‒ 1), (log(k ‒ 1) + k/2 ) and k/2 competitive rate respectively. Through comparison with the competitive rates and experimental results of proposed online algorithms, we observe that the performance of online data retrieval is improved in wireless networks.

Data retrieval for deadline-based multi-request in MIMO wireless networks

Data retrieval problem refers to generates an access pattern for downloading a request with multiple data items among the parallel channels such that the efficiency of wireless networks is improved. Although many related papers have discussed this problem that clients require to retrieve one request by one and multiple antennae, but there are few study on it that clients equipped with multiple antennae require to retrieve multiple requests, in particular that each request has time constraint. So, this paper proposes an algorithm that schedules the suitable antennae to find a retrieval sequence about these requests for keeping the balance of access latency. For retrieving each request, we develop an efficient scheme that adopts maximum match to generate an access pattern for downloading all requested data items so that the access latency of each request and deadline miss ratio are minimised. Through experiments, the proposed algorithms keep good performance.

Online algorithm for data retrieval with multiple antennae in wireless communication

Given a set of requested data items and a set of multiple channels, online multi-antennae data retrieval problem (OMAP) is to download all requested data items from these channels when the clients are equipped with multiple antennae and do not know any a-prior knowledge of wireless data broadcast system, such that the total access latency is minimised and the access latency among all antennae keeps balance. So this paper proposes online multiple antennae data retrieval algorithm based on requested ratio (OMR) and online multiple antennae retrieval algorithm for equal data items based on unretrieved ratio (OMU) that introduces the requested ratio and unretrieved ratio respectively when the length of data items is equal. In addition, when the length of data items is unequal, the paper proposes online multiple antennae retrieval algorithm for unequal data items based on length ratio (OML) that introduces the length to compute the length ratio. Finally, we analyse the competitive rates of three algorithms. Through experiments, the proposed schemes can have currently better efficiency by comparing with some existing schemes and solve OMAP.

Online algorithm for data retrieval with multiple antennae in wireless communication

Given a set of requested data items and a set of multiple channels, online multi-antennae data retrieval problem (OMAP) is to download all requested data items from these channels when the clients are equipped with multiple antennae and do not know any a-prior knowledge of wireless data broadcast system, such that the total access latency is minimised and the access latency among all antennae keeps balance. So this paper proposes online multiple antennae data retrieval algorithm based on requested ratio (OMR) and online multiple antennae retrieval algorithm for equal data items based on unretrieved ratio (OMU) that introduces the requested ratio and unretrieved ratio respectively when the length of data items is equal. In addition, when the length of data items is unequal, the paper proposes online multiple antennae retrieval algorithm for unequal data items based on length ratio (OML) that introduces the length to compute the length ratio. Finally, we analyse the competitive rates of three algorithms. Through experiments, the proposed schemes can have currently better efficiency by comparing with some existing schemes and solve OMAP.

Data retrieval for deadline-based multi-request in MIMO wireless networks

Data retrieval problem refers to generates an access pattern for downloading a request with multiple data items among the parallel channels such that the efficiency of wireless networks is improved. Although many related papers have discussed this problem that clients require to retrieve one request by one and multiple antennae, but there are few study on it that clients equipped with multiple antennae require to retrieve multiple requests, in particular that each request has time constraint. So, this paper proposes an algorithm that schedules the suitable antennae to find a retrieval sequence about these requests for keeping the balance of access latency. For retrieving each request, we develop an efficient scheme that adopts maximum match to generate an access pattern for downloading all requested data items so that the access latency of each request and deadline miss ratio are minimised. Through experiments, the proposed algorithms keep good performance.