Management of data with a time dimension increases the overhead of storage and query processing in large database applications especially with the join operation, which is a commonly used and expensive relational operator. The temporal join evaluation can be time consuming because temporal data are intrinsically multi-dimensional. Also, due to a limited buffer size, the long-lived data can be frequently swapped-in and swapped-out between disk and main memory thus resulting in a low cache hit ratio. The proposed index-based Hilbert-Temporal Join (Hilbert-TJ) join algorithm maps temporal data into Hilbert curve space that is inherently clustered, thus allowing for fast retrieval and storage. This paper also proposes the Adaptive Replacement Cache-Temporal Data (ARC-TD) buffer replacement policy which favors the cache retention of data pages in proportion to the average life span of the tuples in the buffer. By giving preference to tuples having long life spans, a higher cache hit ratio can be achieved. The caching priority is also balanced between recently and frequently accessed data. The comparison study consists of different join algorithms and buffer replacement policies. Additionally, the Hilbert-TJ algorithm offers support to both valid-time and transaction-time data.