Abstract—An efficient multiversion access structure for a transaction-time database is presented. Our method requires optimal storage and query times for several important queries and logarithmic update times. Three version operations}inserts, updates, and deletes}are allowed on the current database, while queries are allowed on any version, present or past. The following query operations are performed in optimal query time: key range search, key history search, and time range view. The key-range query retrieves all records having keys in a specified key range at a specified time; the key history query retrieves all records with a given key in a specified time range; and the time range view query retrieves all records that were current during a specified time interval. Special cases of these queries include the key search query, which retrieves a particular version of a record, and the snapshot query which reconstructs the database at some past time. To the best of our knowledge no previous multiversion access structure simultaneously supports all these query and version operations within these time and space bounds. The bounds on query operations are worst case per operation, while those for storage space and version operations are (worst-case) amortized over a sequence of version operations. Simulation results show that good storage utilization and query performance is obtained. Index Terms—Transaction-time database, multidimensional data, access methods, data structures, indexing, I/O complexity.

We present a practical and asymptotically optimal indexing structure for a versioned timestamped database with step-wise constant data. Three version operations, insertions, updates, and deletes are allowed for the present version, whereas query operations are allowed for any version, present or past. Snapshot and time-range queries can be answered optimally with this structure. As a two-level index, attribute-search and attribute-history queries can be solved in time proportional to the output size plus an additive logarithmic term. The time index uses linear storage; this improves upon previous work which either had logarithmic query overhead time and quadratic space, or linear space and linear query overhead time. The tradeoff is a small increase in the time for version operations from constant to logarithmic. All measures are worst-case. The index has a natural structure for archiving in write-once storage media like optical disks. Research supported in part by NSF grants CCR90103...

We present an asymptotically optimal and practically efficient multiversion access structure (MVAS) for a versioned timestamped database with step-wise constant data. The structure combines an enhanced B+-tree with access lists in a novel way. This allows for both key history and version range queries to be answered optimally, while still maintaining linear storage. In our model, three version operations, insertions, updates, and deletes are allowed for the present version, whereas query operations are allowed for any version, present or past. The following query operations are supported optimally: key search, key range search, key history search (or time range search), snapshot of the database, and time range view. The bounds on storage space and query operations are worst case bounds per operation, while those for version operations are amortized over a sequence of version operations. Partially supported by NSF and DARPA Grant CCR 9006300 and NSF Grant CCR 9010366. y Dept. of EC...

This paper compares different indexing techniques proposed for supporting efficient access to temporal data. The comparison is based on a collection of important performance criteria, including the space consumed, update processing, and query time for representative queries. The comparison is based on worst-case analysis, hence no assumptions on data distribution or query frequencies are made. When a number of methods have the same asymptotic worst-case behavior, features in the methods that affect average case behavior are discussed. Additional criteria examined are the pagination of an index, the ability to cluster related data together, and the ability to efficiently separate old from current data (so that larger archival storage media such as write-once optical disks can be used). The purpose of the paper is to identify the difficult problems in accessing temporal data and describe how the different methods aim to solve them. A general lower bound for answering basic temporal queries is also introduced.