Required Storage Space Research Articles

IMPLEMENTASI METODE EVEN-RODEH CODE UNTUK KOMPRESI KITAB UNDANG-UNDANG HUKUM PIDANA (KUHP) BERBASIS ANDROID

Smartphones in addition to being a smartphone communication tool also functions as a tool for finding information. One application that is currently widely used by students and the public is KUHP application that run in mobile. KUHP application is an application that contains material of criminal laws. The KUHP application has large material and is stored in the application database after it is installed. The large size of the database of the Criminal Code application causes the need for large memory space at mobile. This has become one of the causes of smartphone memory performance to be reduced and slow. Compression technique can be used as a solution to the problem above. Compression aims to compress large data sizes into smaller ones, so that the required storage space decreases. This study describes how to do the compression process of the KUHP application material based on the Even Rodeh Code algorithm. Based on the results of tests conducted to compress 280 bits of data, it was found that the size of the data after being compressed was 176 bits, with compression ratio is 62.8%, ratio of compression is 1.59 bits, and redundancy is 37.2%.Keywords: Compression, KUHP Aplication, Even Rodeh Code, Database

A New Asymmetrical Encryption Algorithm Based on Semitensor Compressed Sensing in WBANs

Wireless body area networks (WBANs) are applied to monitor patients remotely. The sensors in WBANs have the characteristics of limited computing and less memory, while requiring real-time and security communications between sensors. In order to deal with the above problems, this article proposes a new asymmetric cryptographic algorithm (Diffie–Hellman–Hash-compression, abbreviated as DHS-C), which is based on the matrix decomposition. The asymmetric cryptographic algorithm can effectively solve the robustness problem in WBANs. The implementation of semitensor-compressed sensing can encrypt multiple signals with different dimensions and reduce the amount of transmission. In addition, the hash function, Arnold scrambling, and chaotic scrambling are applied to improve the security of our algorithm. A series of simulation and security analysis, including key space, pixel distribution of the encrypted image, adjacent pixel correlation, required storage space, compression ratio, peak-signal-to-noise ratio, and so on, are given to show the better performance of our proposed scheme.

Using space-filling curves to improve the quad tree for spatial indexing

Spatial indexes, like the ones that are based on Quad Tree, are important in spatial data-bases for the effective implementation of queries with spatial constraints, particularly in the case where queries include spatial links. The quad trees are a very interesting subject, given the fact that they give the ability to solve problems in a way that focuses only on the important areas with the highest density of information. But it is not without the disadvantages because the search process in the quartile suffers from the problem of repetition when reaching the terminal node and return to the behaviour of another way in the search and lead to the absorption of large amounts of time and storage. A database management system can handle data very easily if the object is one-dimension (sequential).
 In this paper, improve the quad tree by combining one of the space filling curve types, including the Hilbert curve and the Z-ordering curve with a quad tree. It will convert from two-dimensional to one-dimensional and sequentially search and end the problem of repetition whenever it reaches a terminal node Ordinary quad tree. Resulting in reduced storage space requirements and improved implementation time.

Bi-objective optimization of the joint replenishment problem in a two-echelon supply chain

In this research, a bi-objective optimisation model for the joint replenishment problem (JRP) of a supply chain comprised of a single supplier and multiple retailers is developed, in which the retailers are assumed to be members of a unique distribution company. The mathematical model minimises the supplier's as well as the retailers' cost subject to some constraints. The constraints are the required storage spaces for any retailer, for any product, and for all the products. The benefit of using the JRP policy is shown based on minimising total cost of supplier and retailers. Since the developed model of the problem is NP-hard, the multi-objective meta-heuristic optimisation algorithm of non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to solve the problem. Besides, since no benchmark is available in the literature to verify and validate the results obtained, a non-dominated ranking genetic algorithm (NRGA) is suggested to solve the problem as well. For further validation, the Pareto fronts are compared to lower and upper bounds obtained using a genetic algorithm employed to solve two single-objective problems separately.

Deep Binary Reconstruction for Cross-Modal Hashing

To satisfy the huge storage space and organization capacity requirements in addressing big multimodal data, hashing techniques have been widely employed to learn binary representations in cross-modal retrieval tasks. However, optimizing the hashing objective under the necessary binary constraint is truly a difficult problem. A common strategy is to relax the constraint and perform individual binarizations over the learned real-valued representations. In this paper, in contrast to conventional two-stage methods, we propose to directly learn the binary codes, where the model can be easily optimized by a standard gradient descent optimizer. However, before that, we present a theoretical guarantee of the effectiveness of the multimodal network in preserving the inter- and intra-modal consistencies. Based on this guarantee, a novel multimodal deep binary reconstruction model is proposed, which can be trained to simultaneously model the correlation across modalities and learn the binary hashing codes. To generate binary codes and to avoid the tiny gradient problem, a novel activation function first scales the input activations to suitable scopes and, then, feeds them to the tanh function to build the hashing layer. Such a composite function is named adaptive tanh . Both linear and nonlinear scaling methods are proposed and shown to generate efficient codes after training the network. Extensive ablation studies and comparison experiments are conducted for the image2text and text2image retrieval tasks; the method is found to outperform several state-of-the-art deep-learning methods with respect to different evaluation metrics.

Research on some key technologies of Hadoop’s cloud GIS

Cloud computing is the product of the development of Internet computing to a certain stage, is the latest result of the evolution of parallel computing, grid computing and other new computing methods. The infinite expansion of cloud computing storage technology can meet the rapid growth of spatial data storage space requirements, powerful computing capabilities for spatial information retrieval, processing, analysis and other high-speed services to ensure. Aiming at the problems of massive data storage, processing, analysis and continuous service faced by GIS, this paper constructs the application of Hadoop-based GIS, and studies some key technologies.

DASH

In this work, we propose Database Shadowing, or DASH, which is a new crash recovery technique for SQLite DBMS. DASH is a hybrid mixture of classical shadow paging and logging. DASH addresses four major issues in the current SQLite journal modes: the performance and write amplification issues of the rollback mode and the storage space requirement and tail latency issues of the WAL mode. DASH exploits two unique characteristics of SQLite: the database files are small and the transactions are entirely serialized. DASH consists of three key ingredients Aggregate Update, Atomic Exchange and Version Reset. Aggregate Update eliminates the redundant write overhead and the requirement to maintain multiple snapshots both of which are inherent in the out-of-place update. Atomic Exchange resolves the overhead of updating the locations of individual database pages exploiting order-preserving nature of the metadata update operation in modern filesystem. Version Reset makes the result of the Atomic Exchange durable without relying on expensive filesystem journaling. The salient aspect of DASH lies in its simplicity and compatibility with the legacy. DASH does not require any modifications in the underlying filesystem or the database organization. It requires only 451 LOC to implement. In Cyclomatic Complexity score, which represents software complexity, DASH renders 33% lower (simpler) mark than PERSIST and WAL modes of SQLite. We implement DASH for SQLite on Android and extensively evaluate it on widely used smartphone devices. DASH yields 4x performance gain over PERSIST mode (default journaling mode). Compared to WAL mode (the fastest journaling mode), DASH uses only 2.5% of the storage space on average. The transaction latency of DASH at 99.9% is one fourth of that of WAL mode.

Nucleotide Archival Format (NAF) enables efficient lossless reference-free compression of DNA sequences.

SummaryDNA sequence databases use compression such as gzip to reduce the required storage space and network transmission time. We describe Nucleotide Archival Format (NAF)—a new file format for lossless reference-free compression of FASTA and FASTQ-formatted nucleotide sequences. Nucleotide Archival Format compression ratio is comparable to the best DNA compressors, while providing dramatically faster decompression. We compared our format with DNA compressors: DELIMINATE and MFCompress, and with general purpose compressors: gzip, bzip2, xz, brotli and zstd.Availability and implementationNAF compressor and decompressor, as well as format specification are available at https://github.com/KirillKryukov/naf. Format specification is in public domain. Compressor and decompressor are open source under the zlib/libpng license, free for nearly any use.Supplementary information Supplementary data are available at Bioinformatics online.

Three Dimensional Point Cloud Compression and Decompression Using Polynomials of Degree One

The availability of cheap depth range sensors has increased the use of an enormous amount of 3D information in hand-held and head-mounted devices. This has directed a large research community to optimize point cloud storage requirements by preserving the original structure of data with an acceptable attenuation rate. Point cloud compression algorithms were developed to occupy less storage space by focusing on features such as color, texture, and geometric information. In this work, we propose a novel lossy point cloud compression and decompression algorithm that optimizes storage space requirements by preserving geometric information of the scene. Segmentation is performed by using a region growing segmentation algorithm. The points under the boundary of the surfaces are discarded that can be recovered through the polynomial equations of degree one in the decompression phase. We have compared the proposed technique with existing techniques using publicly available datasets for indoor architectural scenes. The results show that the proposed novel technique outperformed all the techniques for compression rate and RMSE within an acceptable time scale.

A Novel Trajectory-VLAD Based Action Recognition Algorithm for Video Analysis

Recognition of human actions in videos has been an active research area in computer vision community due to its important theoretical research significance and extensive practical application value. As the most commonly used local feature descriptor of video, dense trajectories were shown superior performance for action recognition on a variety of datasets. However, the high computational complexity and huge storage space requirements of the algorithm limit its application scenarios. This paper optimizes the action recognition algorithm based on improved dense trajectories feature, we use Vector of Locally Aggregated Descriptors (VLAD) for feature encoding, which can greatly reduce the computational complexity and avoid expensive hard disk access, at the same time, can also effectively reduce the loss of feature information and improve the recognition accuracy.

R3D3: A Doubly Opportunistic Data Structure for Compressing and Indexing Massive Data

Opportunistic data structures are used extensively in big data practice to break down the massive storage space requirements of processing large volumes of information. A data structure is called (singly) opportunistic if it takes advantage of the redundancy in the input in order to store it in iformationtheoretically minimum space. Yet, efficient data processing requires a separate index alongside the data, whose size often substantially exceeds that of the compressed information. In this paper, we introduce doubly opportunistic data structures to not only attain best possible compression on the input data but also on the index. We present R3D3 that encodes a bitvector of length n and Shannon entropy H0 to nH0 bits and the accompanying index to nH0(1/2 + O(log C/C)) bits, thus attaining provably minimum space (up to small error terms) on both the data and the index, and supports a rich set of queries to arbitrary position in the compressed bitvector in O(C) time when C = o(log n). Our R3D3 prototype attains several times space reduction beyond known compression techniques on a wide range of synthetic and real data sets, while it supports operations on the compressed data at comparable speed.

Storage Consumption Reduction using Improved Inverted Indexing for Similarity Search on LINGO Profiles

Millions of compounds which exist in huge datasets are represented using Simplified Molecular-Input Line- Entry System (SMILES) representation. Fragmenting SMILES strings into overlapping substrings of a defined size called LINGO Profiles avoids the otherwise time-consuming conversion process. One drawback of this process is the generation of numerous identical LINGO Profiles. Introduced by Kristensen et al, the inverted indexing approach represents a modification intended to deal with the large number of molecules residing in the database. Implementing this technique effectively reduced the storage space requirement of the dataset by half, while also achieving significant speedup and a favourable accuracy value when performing similarity searching. This report presents an in-depth analysis of results, with conclusions about the effectiveness of the working prototype for this study.

Deep Compression: A Compression Technology for Apron Surveillance Video

This paper presents a deep compression method that uses some object detection methods to separate moving and stationary objects from a real frame in an apron surveillance video. The extracted object image, the corresponding position information and the background image are stored in a linked list. When the video is decompressed, the extracted object images are restored to the background image according to the corresponding information, and the overall adjustment, such as illumination, is performed according to the stored information. Finally, a video with high similarity to the original video is generated. This video compression method greatly reduces the required storage space without destroying the original video information.

Deep Neural Network Compression for Aircraft Collision Avoidance Systems

One approach to designing decision-making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The resulting collision avoidance strategy can be represented as a numeric table. This methodology has been used in the development of the Airborne Collision Avoidance System X family of collision avoidance systems for manned and unmanned aircraft, but the high-dimensionality of the state space leads to very large tables. To improve storage efficiency, a deep neural network is used to approximate the table. With the use of an asymmetric loss function and a gradient descent algorithm, the parameters for this network can be trained to provide accurate estimates of table values while preserving the relative preferences of the possible advisories for each state. By training multiple networks to represent subtables, the network also decreases the required runtime for computing the collision avoidance advisory. Simulation studies show that the network improves the safety and efficiency of the collision avoidance system. Because only the network parameters need to be stored, the required storage space is reduced by a factor of 1000, enabling the collision avoidance system to operate using current avionics systems.

Neo4j graph database realizes efficient storage performance of oilfield ontology.

The integration of oilfield multidisciplinary ontology is increasingly important for the growth of the Semantic Web. However, current methods encounter performance bottlenecks either in storing data and searching for information when processing large amounts of data. To overcome these challenges, we propose a domain-ontology process based on the Neo4j graph database. In this paper, we focus on data storage and information retrieval of oilfield ontology. We have designed mapping rules from ontology files to regulate the Neo4j database, which can greatly reduce the required storage space. A two-tier index architecture, including object and triad indexing, is used to keep loading times low and match with different patterns for accurate retrieval. Therefore, we propose a retrieval method based on this architecture. Based on our evaluation, the retrieval method can save 13.04% of the storage space and improve retrieval efficiency by more than 30 times compared with the methods of relational databases.

Dimensionality reduction of SDPs through sketching

We show how to sketch semidefinite programs (SDPs) using positive maps in order to reduce their dimension. More precisely, we use Johnson–Lindenstrauss transforms to pro- duce a smaller SDP whose solution preserves feasibility or approximates the value of the original problem with high probability. These techniques allow to improve both complexity and storage space requirements. They apply to problems in which the Schatten 1-norm of the matrices specifying the SDP and also of a solution to the problem is constant in the problem size. Furthermore, we provide some results which clarify the limitations of positive, linear sketches in this setting.

A new approach in DNA sequence compression: Fast DNA sequence compression using parallel chaos game representation

DNA sequence is a long string and contains some hidden significant genetic information which are considered by biological researchers in different laboratories, comparing genomes, medicine, engineering and etc. Due to ascending growth of DNA researches, users have faced some challenges in some fields like transfer, maintenance and data storage. Due to the large size of such sequences, there is a need to have a lot of space for storage, so a method is needed to reduce the amount of required space. Data compression may be an efficient way to reduce the size of DNA sequences and results in reduced storage space and transfer bandwidth requirements. Some patterns of effectiveness and importance of methods in compressing data can be seen in compressing existed sequences in database, compressing image and video and some standards like DICOM.The proposed algorithm is a hybrid one consisting of 4 phases: in phase 1 it divides the sequences into subsequences and takes a parallel chaos game representation approach, in phase 2 it replaces the high-frequency substrings using a dictionary method, in phase 3 it uses a parallel Hoffman coding approach, and in phase 4 it creates a structure based on Hoffman results. Since the algorithm runs in parallel mode and creates a dictionary for each subsequence, it increases the compression speed. Also due to the fact that CGR provides all possible patterns, there is no need to search for patterns and results in reduced computation complexity and time. Through the use of this method a benchmarked DNA string “MPOMTCG” gained a compression ratio of 1.6.

Gaussian Approximation-Based Lossless Compression of Smart Meter Readings

Automation metering services, load forecasting, and energy feedback are among the great benefits of smart meters. These meters are usually connected using Narrowband power line communication to transmit the collected waveform readings. The huge volume of these streams, the limited-bandwidth, energy, and required storage space pose a unique management challenge. Compression of these streams has a significant opportunity to solve these issues. Therefore, this paper proposes a new lossless smart meter readings compression algorithm. The uniqueness is in representing smart meter streams using few parameters. This is effectively achieved using Gaussian approximation based on dynamic-nonlinear learning technique. The margin space between the approximated and the actual readings is measured. The significance is that the compression will be only for margin space limited points rather than the entire stream of readings. The margin space values are then encoded using burrow-wheeler transform followed by move-to-front and run-length to eliminate the redundancy. Entropy encoding is finally applied. Both mathematical and empirical experiments have been thoroughly conducted to prove the significant enhancement of the entropy (i.e., almost reduced by half) and the resultant compression ratio (i.e., 3.8:1) which is higher than any known lossless algorithm in this domain.

Massive Volume of Unstructured Data and Storage Space Optimization- a Review

Nowadays the volume of digital data generated and used by enterprises is increasing at an enormous rate. The survey says that more than 80% of data that were generated in the last two years are unstructured in nature. Hence storage space requirement for storing this big volume of unstructured data is very high. It has gained attention to large-scale storage systems. Deduplication is a space efficient method mainly used to solve storage space optimization problem. This paper focuses on the effect of massive volume of unstructured data and review various storage optimization techniques and survey of various storage types. In addition, it elaborates specific challenges with regard to storage optimization using deduplication and technology that handles a huge amount of unstructured data.

A novel deep hashing method for fast image retrieval

In recent years, the deep hashing image retrieval algorithm has become a hot spot in current research. Although the deep hashing algorithm has achieved good results in image retrieval, how to further improve the accuracy of the image retrieval algorithm and reduce the computational complexity of the algorithm, the two basic requirements of the algorithm, need attention in image retrieval. The paper proposes a new Aggregate Deep Fast Supervised Discrete Hashing (ADFSDH) method for highly efficient image retrieval on large-scale datasets. Specifically, in order to improve the algorithm performance, the paper first proposes a new Aggregate Deep Convolutional Neural Network (ADCNN) mode based on VGG16, VGG19 and transfer learning for effective image feature extraction, which contains two different feature extractors in parallel. And then, the paper proposes a new feature fusion method. When our weighted proportion is consistent with the Mean Average Precision results of two different feature extractors, we can obtain the most accurate description of the image. Firstly, in order to save ADCNN required storage space and improve ADCNN image retrieval efficiency, the Fast Supervised Discrete Hashing algorithm after adjusting the parameters is introduced into the ADCNN model. In addition, ADFSDH unifies feature learning and hash coding into the same framework. The proposed method was experimented on three datasets (CIFAR10, MNIST and FD-XJ), and the result shows that it is superior to the current mainstream approaches in image retrieval.