Relaxed multi-way trees with group updates
نویسندگان
چکیده
منابع مشابه
Approximately Optimal Trees for Group Key Management with Batch Updates
We investigate the group key management problem for broadcasting applications. Previous work showed that, in handling key updates, batch rekeying can be more cost-effective than individual rekeying. One model for batch rekeying is to assume that every user has probability p of being replaced by a new user during a batch period with the total number of users unchanged. Under this model, it was r...
متن کاملB-trees with relaxed balance
B-trees with relaxed balance have been deened to facilitate fast updating in a concurrent database environment. In that structure, updating and rebalancing are uncoupled such that extensive locking can be avoided in connection with updates. Constraints, weaker than the usual ones, are maintained such that the tree can still be balanced independent of the updating processes. We nd the idea of re...
متن کاملAVL Trees with Relaxed Balance
The idea of relaxed balance is to uncouple the rebalancing in search trees from the updating in order to speed up request processing in main-memory databases. In this paper, we describe a relaxed version of AVL trees. We prove that each update gives rise to at most a logarithmic number of rebalancing operations and that the number of rebalancing operations in the semidynamic case is amortized c...
متن کاملBuilding multi-way decision trees with numerical attributes
Decision trees are probably the most popular and commonly used classification model. They are recursively built following a top-down approach (from general concepts to particular examples) by repeated splits of the training dataset. When this dataset contains numerical attributes, binary splits are usually performed by choosing the threshold value which minimizes the impurity measure used as sp...
متن کاملBoosting with Multi-Way Branching in Decision Trees
It is known that decision tree learning can be viewed as a form of boosting. However, existing boosting theorems for decision tree learning allow only binary-branching trees and the generalization to multi-branching trees is not immediate. Practical decision tree algorithms, such as CART and C4.5, implement a trade-off between the number of branches and the improvement in tree quality as measur...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 2003
ISSN: 0022-0000
DOI: 10.1016/s0022-0000(03)00027-8