In this paper, different approaches are classified and evaluated for optimizing the access to main-memory resident persistent objects---techniques which are commonly referred to as "pointer swizzling ". To speed up the access along inter-object references, the persistent pointers in the form of unique object identifiers (OIDs) are transformed (swizzled) into main-memory pointers (addresses). Pointer swizzling techniques can be directed into two classes: (1) strategies that allow replacement of swizzled objects from the buffer before the end of an application program and (2) those that outrule the displacement of swizzled objects. Whereas the latter class of pointer swizzling methods has received much attention in recent literature, the first class---i.e., techniques that take "precautions" for the replacement of swizzled objects---has not yet been thoroughly investigated. Four different pointer swizzling techniques allowing object replacement were investigated and contrasted with the p...

We classify clustering algorithms into sequence-based techniques---which transform the object net into a linear sequence---and partition-based clustering algorithms. Tsangaris and Naughton [TN91, TN92] have shown that the partition-based techniques are superior. However, their work is based on a single partitioning algorithm, the Kernighan and Lin heuristics, which is not applicable to realistically large object bases because of its high running-time complexity. The contribution of this paper is two-fold: (1) we devise a new class of greedy object graph partitioning algorithms (GGP) whose running-time complexity is moderate while still yielding good quality results. For large object graphs GGP is the best known heuristics with an acceptable running-time. (2) We carry out an extensive quantitative analysis of all well-known partitioning algorithms for clustering object graphs. Our analysis yields that no one algorithm performs superior for all object net characteristics. Therefore, we d...

Clustering is one of the most effective means to enhance the performance of object base applications. Consequently, many proposals exist for algorithms computing good object placements depending on the application profile. However, in an effective object base reorganization tool the clustering algorithm is only one constituent. In this paper, we report on our object base reorganization tool that covers all stages of reorganizing the objects: the application profile is determined by a monitoring tool, the object placement is computed from the monitored access statistics utilizing a variety of clustering algorithms and, finally, the reorganization tool restructures the object base accordingly. The costs as well as the effectiveness of these tools is quantitatively evaluated on the basis of the OO1-benchmark. 1 Introduction Ever since the "early days" of database management systems, clustering has proven to be one of the most effective performance enhancement techniques. Therefore, many...

... performance enhancement techniques for object oriented database management systems. The bulk of the work in the area has been on static clustering algorithms which re-cluster the object base when the database is off-line. However, this type of re-clustering cannot be used when 24-hour database access is required. In such situations on-line clustering is required, which allows the object base to be reclustered while the database is in operation. We believe that most existing on-line clustering algorithms lack three important properties. These include: the use of opportunism to imposes the smallest I/O footprint for re-organisation; the re-use of prior research on static clustering algorithms; and the prioritisation of re-clustering so that the worst clustered pages are re-clustered first. In this paper, we present OPCF, a framework in which any existing off-line clustering algorithm can be made on-line and given the desired properties of opportunism and clustering prioritisation. In addition, this paper presents a performance evaluation of the ideas suggested above and in particular shows the importance of opportunism in improving the performance of on-line clustering algorithms in a variety of situations. The main contribution of this paper is the observation that existing off-line clustering algorithms, when transformed via a simple transformation framework such as OPCF, can produce on-line clustering algorithms that out-perform complex existing on-line algorithms, in a variety of situations. This makes the solution presented in this paper particularly attractive to real OODBMS system implementers who often prefer to opt for simpler solutions.