Enterprise Content Management (ECM) as a unifying concept is relatively new. ECM is based on the integration of a set of technologies each of which is still evolving. Not surprisingly, research is scarce and the market is still consolidating. As a result, integral ECM implementations are still rare.It is argued that, pending the widespread adoption and implementation of ECM, much can be learned from experiences with the implementation of Enterprise Resource Planning (ERP) software.Another reason for taking these experiences seriously is the growing extension of ERP-products with ECM-functionality. This extension may eventually result in an ERP-driven ECM-implementation or even in combined ERP/ECM products.Lessons learned from the implementation of ERP are that implementations may be compromised by a large number of legacy issues. It is argued that the same issues may similarly affect the implementation of ECM. Therefore, it is advised, with due adaptation, to take these issues into account in devising implementation strategies for ECM.

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This paper focuses on improving decision tree induction algorithms when a kind of tie appears during the rule generation procedure for specific training datasets. The tie occurs when there are equal proportions of the target class outcome in the leaf node s records that leads to a situation where majority voting cannot be applied. To solve the above mentioned exception, we propose to base the prediction of the result on the naive Bayes (NB) estimate, k-nearest neighbour (k-NN) and association rule mining (ARM). The other features used for splitting the parent nodes are also taken into consideration.

In multiuser Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems the performance can be improved when full Channel State Information (CSI) is available at the transmitter. The characterization of downlink channel is estimated at the receiver and feedback to the transmitter. But the transmitter may acquire false information due to delay or errors in the feedback channel. The resulting channel capacity loss and Bit Error Rate (BER) performance are evaluated. The BER and the capacity of MIMO-OFDM systems are analyzed by varying the parameters of the system.