Data Recovery Tools Research Articles

Effective Strategies for Managing Multi-Cloud Salesforce Solutions

Businesses are using multi-cloud solutions to improve flexibility, scalability, and efficiency in today's dynamic environment. Salesforce, a prominent CRM platform, is crucial to many organizations' multi-cloud ecosystems. To achieve seamless integration, maximum performance, and safe operations, administering Salesforce products across numerous cloud environments requires distinct methodologies. This paper discusses best practices, essential concerns, and practical solutions for managing multi-cloud Salesforce applications. The research starts with multi-cloud advantages and difficulties. By using various cloud providers, companies may prevent vendor lock-in, improve resilience, and customize solutions. These benefits complicate data management, integration, and security. The article begins with multi-cloud Salesforce integration techniques. It emphasizes the need for a smooth integration architecture between Salesforce and other cloud services. API-based integrations, middleware platforms, and cloud-based integration services are tested for real-time data synchronization and latency. The study also examines data integration technologies and their influence on data consistency and accuracy across numerous cloud environments. The second segment handles multi-cloud Salesforce data management issues. It addresses data governance, quality control, and storage optimization. Strong data management practices that meet industry and regulatory standards are prioritized. The study examines data transfer, backup, and recovery tools and methods that enable effective data management across cloud platforms.

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle to improving the accuracy and efficiency of hydrological analysis. To address this issue, we developed a machine learning algorithm-based precipitation data recovery tool to detect and predict missing precipitation data at observatories. This study investigated 30 weather stations in South Korea, evaluating the applicability of machine learning algorithms (artificial neural network and random forest) for precipitation data recovery using environmental variables, such as air pressure, temperature, humidity, and wind speed. The proposed model showed a high performance in detecting the missing precipitation occurrence with an accuracy of 80%. In addition, the prediction results from the models showed predictive ability with a correlation coefficient ranging from 0.5 to 0.7 and R2 values of 0.53. Although both algorithms performed similarly in estimating precipitation, ANN performed slightly better. Based on the results of this study, we expect that the machine learning algorithms can contribute to improving hydrological modeling performance by recovering missing precipitation data at observation stations.

User structural information in priority-based ranking for top-N recommendation

The recommender system is a set of data recovery tools and techniques used to recommend items to users based on their selection. To improve the accuracy of the recommendation, the use of additional information (e.g., social information, trust, item tags, etc.) in addition to user-item ranking data has been an active area of research for the past decade. In this paper, we present a new method for recommending top-N items, which uses structural information and trust among users within the social network and extracts the implicit connections between users and uses them in the item recommendation process. The proposed method has seven main steps: (1) extract items liked by neighbors, (ii) constructing item features for neighbors, (iii) extract embedding trust features for neighbors, (iv) create user-feature matrix, (v) calculate user’s priority, (vi) calculate item’s priority and finally, (vii) recommend top-N items. We implement the proposed method with three datasets for recommendations. We compare our results with some advanced ranking methods and observe that the accuracy of our method for all users and cold-start users improves. Our method can also create more items for cold-start users in the list of recommended items.

A Customized Data Recovery Tool

Data recovery is the process of salvaging deleted, formatted, corrupted, damaged or inaccessible data from the storage media as it cannot be accessed through normal way. There are two types of data recovery which is physical recovery and logical recovery. Physical recovery needs special equipment to repair the damaged parts of storage device like scratched plates, stalled spindle, fried chip, etc. and this process need to be undertaken in clean and controlled environment to extract data from storage device. Logical recovery is a software-based solution that recovers data from storage device which faced logical error in operating system or accidental user deletion. This paper will discuss on logical recovery and it is divided into few sections which is public awareness on data recovery, literature review on existing data recovery tool, analysis on user requirements for data recovery tool, proposed data recovery tool functionality and proposed data recovery tool framework.

Spiffy

Many file-system applications such as defragmentation tools, file-system checkers, or data recovery tools, operate at the storage layer. Today, developers of these file-system aware storage applications require detailed knowledge of the file-system format, which requires significant time to learn, often by trial and error, due to insufficient documentation or specification of the format. Furthermore, these applications perform ad-hoc processing of the file-system metadata, leading to bugs and vulnerabilities. We propose Spiffy, an annotation language for specifying the on-disk format of a file system. File-system developers annotate the data structures of a file system, and we use these annotations to generate a library that allows identifying, parsing, and traversing file-system metadata, providing support for both offline and online storage applications. This approach simplifies the development of storage applications that work across different file systems because it reduces the amount of file-system--specific code that needs to be written. We have written annotations for the Linux Ext4, Btrfs, and F2FS file systems, and developed several applications for these file systems, including a type-specific metadata corruptor, a file-system converter, an online storage layer cache that preferentially caches files for certain users, and a runtime file-system checker. Our experiments show that applications built with the Spiffy library for accessing file-system metadata can achieve good performance and are robust against file-system corruption errors.

Contemplation of Expunged Data with the help of Digital Forensic Toolkits

Objective: The main motto of this work is to trace the cyber fraud people and moreover we had extended this work in multiple way usage in the forensic mode. Method: So in this method let us imagine a scenario in which something incorrectly happen on the server and on the off chance that if an association will misfortune some kind of information (means if an association will hack and misfortune some critical information. Yes this may happen, it may happen in light of the fact that an assailant more keen than an infiltration analyzer. So after this the time is to get the programmer, for this reason you require a scientific device, so in this article we will talk about DEFT Linux a complete distro for measurable purposes. Findings: The Digital Evidence and Forensic Toolkit or DEFT Linux, is a live distro that gives devices to investigating PC frameworks and gathering criminological confirmation. The distro contains apparatuses for scanning and breaking down circle drives, databases and system movement of Windows and Linux frameworks. Application: DEFT incorporates instruments to bolster Incident Response, Cyber Intelligence and Computer Forensics. It incorporates WINE and the Digital Advanced Response Toolkit (DART). The essential center of the distro is the precise and dependable gathering of proof for use in criminal procedures. Thus, most DEFT devices are arranged to gathering data without changing the framework being analyzed. And more over we had used FTK imager and Winhex, data recovery tools to find suspect. So, finally in this paper we are going to test an unapproved information with a planned results.

Computer Forensic Investigation on Hard Drive Data Recovery: A Review Study

Computer Forensics is a science of seeking evidence found in computers and digital storage media. The main challenge before computer forensic investigatorsis to examine digital media with the aim of finding, collecting, preserving, recovering, analyzing and presenting facts about digital information and guaranteeing its accuracy and reliability.It is said that, Once it is has been deleted it is gone forever! This statement is not true. Deleted files can actually be recovered if effort to do so is made shortly after deletion. There are several tools and methods by which we can easily recover our data and get our data as it is back. This paper includes the various methods and tools to recover data from Hard Disk Drive (HDD), how data recovery tools works, in what situation you can lost your data permanently and in what conditions you can recover your data back. You can know about types of damages and techniques by which data can be recovered by this paper. This paper gives a glance of various methods used to recover data from Hard Disk Drive (HDD) and their corresponding forensic approach done by the computer forensic experts in the perspective of recovery.

디지털 포렌식 관점의 데이터 복구 및 분석 프레임워크

대부분의 디지털 포렌식 관련 도구들은 저장매체의 할당된 영역에 존재하는 파일을 분석하는데 초점을 맞추고 있기 때문에 용의자가 의도적으로 삭제하거나 이전에 사용되었던 파일들을 복구하는 기능이 부족하다. 따라서 삭제된 파일을 효과적으로 분석하기 위해서는 데이터 복구도구로 얻은 결과물을 다시 디지털 포렌식 도구로 분석해야한다. 이러한 작업은 사건을 빠르고 효과적으로 대응하거나 데이터의 무결성을 보존하는데 적절하지 않다. 따라서 본 논문에서는 기존 디지털 포렌식 관련 도구들의 한계점을 벗어나 디지털 포렌식 관점에서 데이터 복구 및 분석 도구를 통합 구현하기 위한 프레임워크를 제시하고 구현하였다. Most of digital forensics tools focus on file analysis of allocated area on storage. So, there is a lack of recovery methods for deleted files by suspects or previously used files. To efficiently analyze deleted files, digital forensic tools depend on data recovery tools. These process not appropriate for quick and efficient responses the incident or integrity preservation. This paper suggests the framework for data recovery and analysis tools from digital forensics point of view and presents implementation results.

Data Management: Data Destruction and Preservation, Part 2

Part 1 of this article introduced you to data destruction and data preservation as a concept. In addition, you learned about data recovery and destruction tools, media lifetimes, and other preservation issues.

Site Surface Characteristics and Functional Inferences

With the rise in importance of the archaeological survey as a major data-recovery tool has come greater concern over methods for making inferences about site subsurface characteristics from surface materials. One growing area of concern is the identification of site function using surface features and artifact assemblages. Here we examine the relations among architectural site types (defined using surface features), modeled functional site types, and surface artifact assemblage characteristics. Analysis proceeded in three stages, each one built on the previous stage. In the first stage we used site type models to predict surface assemblage differences between types and then tested these predictions with the surface assemblages from surveyed sites grouped by architecture type. The procedures involved analysis of variance, difference-of-means tests, and discriminant analysis. In the second stage, we used discriminant analysis to create a classification function for predicting site type on the basis of artifact assemblages. In the third stage of the analysis, we investigated possible reasons for the uneven classification results. We conclude our analyses by presenting an example of functional change that affects the ability of the discriminant analysis to distinguish between two of the site types.