Consider the following self-organizing rule called POS(i): after a book in the jth position of a shelf is borrowed, it is moved up one position if ji, and is moved to the ith position if j > i. This is a family of move-forward rules, with POS(l) being the move-to-front rule and POS(n − 1) being the transposition rule where n is the number of books to be organized. We derive explicitly the stationary distribution under the POS(i) rule and show that its search cost compares favorably with that of move-to-front rule under any book access probabilities p1, p1, ···, pn.

We consider the problem of reorganizing a linear list, when the individual queries consist of accesses to a subset of the elements stored, as opposed to the individual elements themselves. In this paper, which to our knowledge represents the first reported result in this model of query processing, we first propose a simple model for a query generator which emits set queries. Subsequently, we present extensions to the classical move-to-front (MTF) and transposition (TR) rules under this generalized query generation mechanism and analyze their performance.

Consider a file with n records denoted by R1, R2, …, Rn. At each access of a record, the file has to be searched sequentially and it is assumed that the search cost is proportional to the number of probes needed to retrieve the records. The access probabilities p1, p2, …, pn are assumed to be unknown constants and accesses are assumed to be independent. A move-forward self-organizing rule moves a record accessed in the ith position to the lith position without changing the relative ordering of other records where li, = 1, li, < i for i = 2, …, n and li+1≧li. A move-forward rule R is said to be ≦ another move-forward rule R′ if l′i≦li for all i. It is shown that when p2 = p3 = ··· = pn, R ≦ R′, implies cost R ≦ cost R′. This is a generalization of some known results. A new consequence is that the move-up-k + 1 rule is more costly than the move-up-k rule and the move-to-front rule is the most costly of all move-forward rules.

We analyze certain self-organizing filing techniques when accesses are assumed to be dependent on each other. The stream of requests for accessing records in a file is modelled as a Markov chain. A general framework is introduced to obtain the asymptotic search cost of a memory-free self-organizing heuristic. The move-to-front heuristic is studied in detail. A formula for the asymptotic search cost, which generalizes that in the case of independent accesses, is obtained. Numerical examples on the performance of the transposition heuristic are provided, and compared with that of the move-to-front heuristic.

A number of items are arranged in a line. At each unit of time one of the items is requested, the i th being requested with probability Pi. We consider rules which reorder the items between successive requests in a fashion which depends only on the position in which the most recently requested item was found. It has been conjectured that the rule which always moves the requested item one closer to the front of the line minimizes the average position of the requested item. An example with six items shows that the conjecture is false.

Suppose that we are given a set of n elements which are to be arranged in some order. At each unit of time a request is made to retrieve one of these elements — the ith being requested with probability Pi. We show that the rule which always moves the requested element one closer to the front of the line minimizes the average position of the element requested among a wide class of rules for all probability vectors of the form P1 = p, P2= · ·· = Pn = (1 – p)/(n − 1). We also consider the above problem when the decision-maker is allowed to utilize such rules as ‘only make a change if the same element has been requested k times in a row', and show that as k approaches infinity we can do as well as if we knew the values of the Pi.