In this paper we provide a concurrency control and recovery (CCR) mechanism over cached LDAP objects. An LDAP server can be directly queried using system calls to retrieve data. Existing LDAP implementations do not provide CCR mechanisms. In such cases, it is up to the application to verify that accesses remain serialized. Our mechanism provides an independent layer over an existing LDAP server (Sun One Directory Server), which handles all user requests, serializes them based on 2 Phase Locking and Timestamp Ordering mechanisms and provides XML-based logging for recovery management. Furthermore, while current LDAP servers only provide object-level locking, our scheme serializes transactions on individual attributes of LDAP objects (attribute-level locking). We have developed a Directory Enabled Network (DEN) Simulator that operates on a subset of directory objects on an existing LDAP server to test the proposed mechanism. We perform experiments to show that our mechanism can gracefully address concurrency and recovery related issues over and LDAP server.
Technical analysis of financial markets describes many patterns of market behavior. For practical use, all these descriptions need to be adjusted for each particular trading session. In this paper, we develop a data mining tool for technical analysis of the futures markets (DMT-TAFM), which dynamically generates rules based on the notion of the price pattern similarity. The tool consists of three main components. The first component provides visualization of data series on a chart with different ranges, scales, and chart sizes and types. The second component constructs pattern descriptions using sets of polynomials. The third component specifies the training set for mining, defines the similarity notion, and searches for a set of similar patterns. DMT-TAFM is useful to prepare the data, and then reveal and systemize statistical information about similar patterns found in any type of historical price series. We performed experiments with our tool on three decades of trading data fro hundred types of futures. Our results for this data set shows that, we can prove or disprove many well-known patterns based on real data, as well as reveal new ones, and use the set of relatively consistent patterns found during data mining for developing better futures trading strategies.
With the increase in popularity of the Internet, the latency experienced by an individual, while accessing the Web, is increasing. In this paper, we investigate one approach to reducing latency by increasing the hit rate for a web cache. To this effect, we developed a predictive model for pre- fetching and a modified Least Recently Used (LRU) method called AssocLRU. This paper investigates the application of a data mining technique, called Association rules to the web domain. The association rules, predict the URLs a user might reference next, and this knowledge is used in our web caching and pre-fetching model. We developed a trace driven cache simulator to compare the performance of our predictive model with the widely used replacement policy, namely, LRU. The traces we used in our experiments were the traces of Web proxy activity taken at Virginia Tech and EPA HTTP. Our results show that our predictive pre-fetching model using association rules achieves a better hit rate than both LRU and AssocLRU.