The algorithm sequential access method

THIS issue of the IEEE TRANSACTIONS ON COMPUTERS iS devoted to the relatively new field of database machine design. What is a database machine? This question is answered in the context of the traditional approach to the storage, retrieval, and update of information. A collection of data is called a file, and the file is composed of individual records. The records themselves are partitioned into fields. In punched card technology, a file is a deck of cards, a record is a single card (a unit record), and a field is a grouping of adjacent columns to which the designer has assigned some meaning. Simple data processing systems access files in a sequential manner, processing each record in turn. This technique is well suited to the relatively inexpensive card and tape storage media. In the more expensive direct access (drum and disk) storage technologies, the storage media is divided into tracks. Tracks can be addressed directly, which leads to the concept of constructing an index of the file which indicates what data are stored on each track. With direct access storage devices and suitable file organization techniques, data processing programs can search, update, insert, and delete records from a file without processing the entire file in a sequential fashion. Direct access storage devices provide the user with more powerful functions, but these functions must be programmed on the central processor, as the IO interface traditionally communicates control information at a very low level.

Integrated optical recording head concepts show promise for future light-weight and compact optical heads. Several fundamental issues remain to be explored before fully integrated optical heads. become a reality. This article reviews the basic operating principles of integrated optic heads, focusing on the materials challenges inherent in developing both partially and fully integrated optical heads.Currently available optical mass storage systems provide a feature unavailable in magnetic direct access storage devices (DASD) — removable and interchangeable media. At present, however, this feature is bought at a price, with magnetic storage devices currently outperforming their optical counterparts in access speed and volumetric storage density. The access time of optical drives is greater than 40 ms, compared to the 10–20 ms typical of magnetic DASD. This is, in large part, a consequence of the 50–100 g moving mass of the optical head. The optical head also limits the maximum volumetric storage density, with current heads ranging from about 0.5 cm to more than 2 cm in height.

Data collection libraries play an important role in component-based software development. The collections contained in those libraries (JCF, STL, LEDA, etc.) implement a mathematical model that defines one or more access methods to the elements (access by key, access to the element stored last, etc.). In addition, most of these libraries allow a different, more efficient type of access, which can be direct access (e.g., by means of the position where the element is stored, that is obtained when it is inserted) or sequential access (usually by means of the iterator concept). This type of efficient access presents several risks with respect to criteria such as precision and suitability that are not solved appropriately in the current existing libraries. In this work, we present two design patterns that provide a generic solution to the problem together with their implementation using aspects. The patterns introduce new data types and new operations that provide the libraries with full uniformity and a high extensibility degree. The use ofaspects allows dissociating these types of access and the functionality of the collection, making aspects responsible of the position persistence management, modification monitoring during iterations, etc. The proposal is implemented using AspectJ.

An Introduction to Direct Access Storage Devices

In recent years, Big Data analytics has become a popular research in computer science and other related data mining areas. It is very important research area in new trends and also creates enormous vibration in technocrats. It is not only vibrant in computer science but also spread into many other diversified areas. In many industrial applications the data storage and retrieval after analysis become a primary task in frequent situations. But achieving at correct time and accurate data become a tedious job at data bases. As the data base sizes are large and extended virtually it is also become difficult to provide security to the data. The data can be quickly accessed with multiplexed memory data base and dedicated, unidirectional storage devices. The accuracy and through put can be improved with parallel processing elements which are involved in super scalar architectures. In the present paper queues are interconnected for faster processing and multiplexed and direct access storage devices are used to access past data for parameter analysis and fetching predefined data values.

The architectural complexity of advanced storage controllers has increased to a point at which architectural verification methods based on document inspections and reviews are no longer effective. To facilitate the architectural verification process, a high-performance simulator (TurboSim) has been developed for architectural-level verification. The TurboSim application includes a set of architectural-level models, which represent essential architectural components, and an automatic test case generator (ATG). The TurboSim ATG is used to generate realistic representations of customer direct access storage device (DASD) track data. The track data are used to drive different TurboSim simulation scenarios. After demonstrating its effectiveness as an architectural verification tool, TurboSim was enhanced to support the automatic generation of hardware test cases. These hardware test cases are used to ensure that the hardware implementation matches architectural specifications.

Customer requirements have guided the evolution and implementation of software and hardware computer data storage technologies. The concept of RAID originated with a group of researchers at the University of California at Berkeley (UCB). They were investigating the use of an array of small capacity, inexpensive personal computer direct access storage devices (DASD) managed by a special type of a controller, as an alternative to the single large expensive disk (SLED) subsystems more common on mainframe computers. The disk arrays can be configured differently to provide different levels of operational capabilities, each providing special advantages in reliability and data availability, speed and economy. Different configurations are referred to as different levels of RAID. Berkeley originally defined a basic set of RAID levels ranging from RAID, level 0 (RAID 0) through RAID, level 5 (RAID 5). The RAID technology vendors have since extended the RAID level domain by defining RAID 6 and beyond to accommodate additional functionality. Each RAID type has shown itself to be suitable for certain applications. The projected reduction in storage costs and facilities, $/GB and ft2/GB respectively, using the present RAID technology and its projected evolution are phenomenal.

Direct access storage devices (DASDs) are widely used in the computer industry to store and manage data. In conventional magnetic recording, an induction head flying very close to the disk surface alters the polarization of the magnetic field of the disk surface to erase and/or write the information on the disk. However, a new technology known as magneto-optical recording or optical recording has considerable promise to increase data densities and reliability of data storage. In magneto-optical storage, magnetic fields are altered by a laser source, which heats the magnetic medium beyond its Curie point, a temperature at which the magnetic medium loses its magnetization. This domain with zero magnetization is subsequently reversed by using an induction magnet. All these processes take place when the disk is rotating at a very high speed with respect to the laser source. An optical disk is a multilayered medium consisting of a thick glass disk on which many layers of different materials are sputtered, only one layer of which serves as a magnetic medium. Therefore, in this paper, a problem of laser-induced heating of a multilayered medium resting on a half-space is considered when the laser is translating with respect to it. The transient heat conduction equation is solved by employing the Laplace transform in the time domain and the Fourier Transform in the x, y dimensions. The resulting ordinary differential equation is solved and the inversion of the Laplace transform is obtained by a technique developed by Crump. The Fourier inversion is obtained by using a Fast Fourier Transform. The technique developed here is then applied to calculate domain size for recorded bits for a given disk, laser power, source characteristics, and rotational velocity.

An automated optical mass storage system (optical MSS) with high-speed magnetooptical (MO) disk drives has been developed. It features a high data transfer rate for writing with the use of the 130-mm ISO standard MO disk, and disk cartridges with high storage efficiency. As the key device, a high-speed MO disk drive has been developed. It provides a high-speed data writing capability of about ten times that of conventional MO disk drives. The optical MSS provides a data transfer rate for reading and writing of 2.1 MB/s, a storage capacity of 250 GB to 1 TB, and an average cartridge handling time of 5 s. From performance simulations, the optical MSS is proved to be applicable to a low-traffic random-access file that stores multimedia data and a high-speed direct-access storage device (DASD) backup file.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The research on the optimization of the illegal access to the database inside and outside the network can effectively improve the security of the network database. Accurate reduction of illegal access to information, the need for accurate collection of known data, and its repeated nuclear iteration, so that the known access to a linear distribution of data to complete the information to restore. The traditional method of data mining into the access to the data filtering process, but the processing of information data linearity difference, resulting in the reduction effect is notgood. Proposed multi-core composite internal and external network illegal access to information collection and accurate reduction method. Set the illegal information collection interval to calculate the address of the database illegal access to information, the use of SSL encryption mechanism to encrypt the information collected, the same type of illegal access to information in the known data for repeated nuclear iteration, to promote similar illegal The access information is linearly distributed in the super-high-dimensional Euclidean space, and the regression equation is integrated into the KRR to complete the illegal access infor- mation acquisition and accurate reduction.The simulation results show that the proposed method has strong robustness and can improve the security of the internal and external network information.

This paper proposes a sequential loading method, which finds the hourly changes of frequency and optimal load distribution between power stations. The method solves to minimize the aggregate cost in the electric power system (EPS). The method also calculates the surplus or deficit of an active power in EPS, hourly power flows of each transmission lines, active power losses in the networks. Computed and analyzed the optimal regimes of Mongolian EPS by the proposed method. The result confirmed that the method can potentially benefit to supply of frequency regulation. Developed software allows doing day-ahead market planning and intraday market preplanning in EPS, which improves electricity market model.

In this paper, we present a new direct access strategy for speaker identification system. DAMClass is a method for direct access strategy that speeds up the identification process without decreasing the identification rate drastically. This proposed method uses speaker classification strategy based on human voice’s original characteristics, such as pitch, flatness, brightness, and roll off. DAMClass decomposes available dataset into smaller sub-datasets in the form of classes or buckets based on the similarity of speaker’s original characteristics. DAMClass builds speaker dataset index based on range-based indexing of direct access facility and uses Nearest Neighbor Search, Range-Based Searching, and Multiclass-SVM Mapping as its access method. Experiments show that the direct access strategy with Multiclass-SVM algorithm outperforms the indexing accuracy of Range-Based Indexing and Nearest Neighbor for one to nine percent. DAMClass is shown to speed up the identification process 16 times faster than sequential access method with 91.05% indexing accuracy.

The Plan S initiative is expected to radically change the market of scholarly periodicals, resulting in the abandoning of the subscription model in favour of the open access model. This transition poses new challenges, as well as sets new tasks for researchers, managers, librarians and scientific publishers, particularly in countries with transforming economies. In this study, we set out to estimate approximate costs and future prospects associated with the transition of Belarus and Ukraine towards open access to scientific information on the example of publications by researchers from these two countries in Elsevier journals. To this end, we accessed the Scopus database and selected all papers affiliated with Belarus and Ukraine published by Elsevier journals in 2018. Subsequently, we established which of these articles indicated a corresponding author from Ukraine or Belarus, as well as collected journal titles, correspondence addresses, access types and the APC fee for each article. In addition, funding sources were established for each open access article. This allowed us to determine the total amount of money that Ukraine and Belarus would have needed to pay so that all the articles of their researchers, which were published in Elsevier journals in 2018, were available in an open-access form. The obtained results show that Belarus and Ukraine are currently under-investing in the support of scientific publications and subscription to scientific resources, which is necessary for making a transition to the model of open access to scientific literature. The proposed method for calculating the expenses of publishing papers written by researchers from a given country in open access can be used in the preparation of transformative agreements with developing countries.

To effectively reduce computational requirements of converted measurement Kalman filters (CMKF), a sequential processing method was presented in this paper. Firstly, the spherical measurement of the target position was converted into Cartesian coordinate domain, resulting in converted measurement error and the corresponding statistics (mean and covariance). Secondly, a sequential processing method which sequentially treated scalar components of the converted measurement vector, was derived based on orthogonal transform. Finally, the proposed sequential processing method was utilized to implement the CMKF algorithm for a target tracking scenario. The simulation results show that the proposed method can effectively reduce the computational requirements of CMKF algorithm while achieving desired tracking performance.

Plant species recognition is one of the important and difficult research areas. Many existing plant recognition and classification methods cannot meet the requirements of the automatic plant recognition system, due to the irregular, complex, and diverse nature of plant leaves. In this paper, a plant recognition approach is proposed by combining CUR decomposition and weighted kernel sparse representation (WKSR). The proposed method is different from traditional plant leaf classification methods. Instead of establishing a classification model by extracting the color, shape, and texture classifying features, the proposed method directly reduces the dimensionality of image and recognizes the test samples based on WKSR coefficients. In order to reduce the recognition time the proposed method uses class specific dictionary learning for sparse modeling. By using the comparison analysis, the proposed method is verified on four plant leaf datasets namely, Flavia leaf dataset, Swedish leaf dataset, Original leaf images and Leafsnap dataset with existing plant classification methods such as Random Forest, Support Vector Machine and K-Nearest Neighbors. The accuracy of the proposed method is 98% which is the best classification rate.