Classical algorithms and data structures assume that the underlying memory is reliable,and the data remain safe during or after processing.However,the assumption is perilous as several studies have shown that large an...Classical algorithms and data structures assume that the underlying memory is reliable,and the data remain safe during or after processing.However,the assumption is perilous as several studies have shown that large and inexpensive memories are vulnerable to bit flips.Thus,the correctness of output of a classical algorithm can be threatened by a few memory faults.Fault tolerant data structures and resilient algorithms are developed to tolerate a limited number of faults and provide a correct output based on the uncorrupted part of the data.Suffix tree is one of the important data structures that has widespread applications including substring search,super string problem and data compression.The fault tolerant version of the suffix tree presented in the literature uses complex techniques of encodable and decodable error-correcting codes,blocked data structures and fault-resistant tries.In this work,we use the natural approach of data replication to develop a fault tolerant suffix tree based on the faulty memory random access machine model.The proposed data structure stores copies of the indices to sustain memory faults injected by an adversary.We develop a resilient version of the Ukkonen’s algorithm for constructing the fault tolerant suffix tree and derive an upper bound on the number of corrupt suffixes.展开更多
Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and...Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and regulatory complexity of healthcare data. This research introduces a tailored partitioning strategy leveraging the MD5 hashing algorithm to enhance data insertion, query performance, and load balancing in healthcare systems. By applying a consistent hash function to patient IDs, our approach achieves uniform distribution of records across partitions, optimizing retrieval paths and reducing access latency while ensuring data integrity and compliance. We evaluated the method through experiments focusing on partitioning efficiency, scalability, and fault tolerance. The partitioning efficiency analysis compared our MD5-based approach with standard round-robin methods, measuring insertion times, query latency, and data distribution balance. Scalability tests assessed system performance across increasing dataset sizes and varying partition counts, while fault tolerance experiments examined data integrity and retrieval performance under simulated partition failures. The experimental results demonstrate that the MD5-based partitioning strategy significantly reduces query retrieval times by optimizing data access patterns, achieving up to X% better performance compared to round-robin methods. It also scales effectively with larger datasets, maintaining low latency and ensuring robust resilience under failure scenarios. This novel approach offers a scalable, efficient, and fault-tolerant solution for healthcare systems, facilitating faster clinical decision-making and improved patient care in complex data environments.展开更多
文摘Classical algorithms and data structures assume that the underlying memory is reliable,and the data remain safe during or after processing.However,the assumption is perilous as several studies have shown that large and inexpensive memories are vulnerable to bit flips.Thus,the correctness of output of a classical algorithm can be threatened by a few memory faults.Fault tolerant data structures and resilient algorithms are developed to tolerate a limited number of faults and provide a correct output based on the uncorrupted part of the data.Suffix tree is one of the important data structures that has widespread applications including substring search,super string problem and data compression.The fault tolerant version of the suffix tree presented in the literature uses complex techniques of encodable and decodable error-correcting codes,blocked data structures and fault-resistant tries.In this work,we use the natural approach of data replication to develop a fault tolerant suffix tree based on the faulty memory random access machine model.The proposed data structure stores copies of the indices to sustain memory faults injected by an adversary.We develop a resilient version of the Ukkonen’s algorithm for constructing the fault tolerant suffix tree and derive an upper bound on the number of corrupt suffixes.
文摘Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and regulatory complexity of healthcare data. This research introduces a tailored partitioning strategy leveraging the MD5 hashing algorithm to enhance data insertion, query performance, and load balancing in healthcare systems. By applying a consistent hash function to patient IDs, our approach achieves uniform distribution of records across partitions, optimizing retrieval paths and reducing access latency while ensuring data integrity and compliance. We evaluated the method through experiments focusing on partitioning efficiency, scalability, and fault tolerance. The partitioning efficiency analysis compared our MD5-based approach with standard round-robin methods, measuring insertion times, query latency, and data distribution balance. Scalability tests assessed system performance across increasing dataset sizes and varying partition counts, while fault tolerance experiments examined data integrity and retrieval performance under simulated partition failures. The experimental results demonstrate that the MD5-based partitioning strategy significantly reduces query retrieval times by optimizing data access patterns, achieving up to X% better performance compared to round-robin methods. It also scales effectively with larger datasets, maintaining low latency and ensuring robust resilience under failure scenarios. This novel approach offers a scalable, efficient, and fault-tolerant solution for healthcare systems, facilitating faster clinical decision-making and improved patient care in complex data environments.
