CloudBik's tenant to tenant migration service offers a comprehensive solution for organizations undergoing mergers, acquisitions, divestitures, or rebranding initiatives. Specializing in seamless transitions between Office 365 tenants, CloudBik ensures the secure and efficient migration of critical data components, including Exchange Online mailboxes, OneDrive for Business files, SharePoint sites, and Microsoft Teams configurations.
This dataset, denoted as š¾_data, represents a synthetic yet structurally authentic warehouse management dataset comprising 4,132 records and 11 well-defined attributes. It was generated using the Gretel.ai platform, following the structural standards provided by the TI Supply Chain APIāStorage Locations specification. The dataset encapsulates essential operational and spatial parameters of warehouses, including unique identifiers, geospatial coordinates, storage capacities, and categorical capacity statuses.
Pavement planar coefficients are critical for a wide range of civil engineering applications, including 3D city modeling, extraction of pavement design parameters, and assessment of pavement conditions. However, existing plane fitting methods often struggle to maintain accuracy and stability in complex road environments, particularly when the point cloud is affected by non-pavement objects such as trees, curbstones, pedestrians, and vehicles. This paper presents REoPC, a robust two-stage estimation method based on road point clouds acquired using hybrid solid-state LiDAR.
Digital technology's pervasive impact is often limited by data silos within institutions. Enabling data sharing via Commonly Accessible Platforms (CAPs) across sectors like healthcare and education promises greater value. However, trust hinges on privacy, security, immutability, and cost-effectiveness. While Blockchain Technology (BCT) offers immutability, a research-practice gap exists due to operational challenges. This paper introduces Vedic Recordchain, a novel multi-hash platform offering an alternative to BCT for CAPs, addressing these challenges.
The shift towards cloud-native applications has been accelerating in recent years. Modern applications are increasingly distributed, taking advantage of cloud-native features such as scalability, flexibility, and high availability. However, this evolution also introduces various security challenges. From a networking perspective, the large number of interconnected components and their intricate communication patterns make detecting and mitigating traffic anomalies a complex task.
Cloud computing has become a relatively new paradigm for the delivery of compute re-
sources, with key management services (KMS) playing a crucial role in securely handling cryptographic
operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, in-
cluding SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryp-
tion/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction
Meituan Bench (MTB) is an enterprise-level benchmarking tool designed for time-series forecasting in real-world business scenarios. Built upon an open-source dataset derived from 10,000 real-world services across various business units, MTB provides a standardized evaluation framework for time-series prediction models. The dataset includes 200 representative services, capturing diverse traffic patterns essential for assessing forecasting performance.
DataVare Software is the well-known brand in the software industry for a number of software services, like Email conversion, Email Migration, Cloud Backup, Exchange Migration, PDF Management, Outlook files management, Address book Manager and many more. We have highly skilled developers who design the software very well with easy and simple users interface. So all kinds of users can operate it very well whether they have technical knowledge or not. Even free demo versions also offered to all users to use this software before buying it full license versions.
Performance models identified at run-time can be used by self-adaptive software systems to execute decisions on a cloud environment. These performance models are built by measuring the control inputs, disturbances, and outputs of the controlled system. These models have been shown to accurately interpolate for data already seen by the model identification method. However, automation in cloud operations can push the environment into operational regions the system has not seen, thus the performance model may not accurately extrapolate into unseen regions.
Due to climate change, the Northwesterner Gilgit Baltistan's, Ghizer district is highly susceptible to glacial lake outburst floods (GLOFs). Nearly 24 GLOFs have occurred in this area in the last ā¼200 years, demonstrating the growing recurrent nature of these incidents. Taking this into account, the assessment of risks associated with GLOFs was investigated in this study. All regional glacial lakes were identified in the first phase, and changes between 2000 and 2023 were mapped using moderate-resolution satellite images (Landsat).
