Case-Base Maintenance: A Streaming Approach

Case Base maintenance can be crucial to CBR system performance. A central current of case base maintenance research focuses on competence-based deletion. Traditionally, deletion is done periodically, pausing CBR system processing to examining the entire case base. However, for streaming data, as often arises in big data contexts, such an approach may be expensive or infeasible. To address this problem, this paper proposes that CBR maintenance can draw on advances from data discovery research. In particular, it presents a case study applying the recent Sieve-Streaming algorithm [2] to enable continuous streaming CBR maintenance to reduce demands on case storage and provide efficient continuous maintenance. The paper presents a preliminary evaluation of this method on the Travel Agent Case Base, and compares it to traditional methods. The experiments are encouraging for the practicality and benefits of the approach for scaleup of case-base maintenance in settings with large-scale data streams.