Wireless Sensor Network (WSN) systems are often used to collect data from the environment for predicting landslide occurrence. However, due to the instability of wireless communications in WSN systems, data loss could occur often. To reconstruct the missing soil moisture data, heterogeneous data with spatio-temporal relations are used, such as soil moisture and rainfall intensity. In the spatial reconstruction method, we take into consideration not only the distance but also the difference of rainfall intensity between two locations. In the temporal reconstruction method, soil moisture will decrease because of evaporation if there is no rainfall. We use historical evaporation rate to reconstruct missing soil moisture data when there is no rainfall. If there is rain, we take a period of sensed soil moisture data to reconstruct any missing data. As a result, we can improve the accuracy of data reconstruction by selecting either spatial or temporal reconstruction results that have a smaller estimation error. The Root Mean Squared Error of spatiotemporal reconstruction is below 2%, even when there is 80% random missing soil moisture data. This is due to both spatiotemporal considerations, as well as, the use of heterogeneous data (rainfall intensity).

Methods for storing and manipulating large rule bases using a relational database management systems (DBMS) are discussed. An approach to decomposing and storing the condition elements in the antecedents of rules such as those used in production rule-based systems is presented. A set-oriented approach, DBCond, which uses a special data structure that is implemented using relations is proposed. A matching algorithm for DBCond uses the relational structures to efficiently identify rules whose antecedents are satisfied. The performance of DBCond is compared with that of DBRete, a DBMS implementation of the Rete match algorithm developed for use with the production rule language OPS5. DBCond is also compared with DBQuery, a method that is based on evaluating queries corresponding to the conditions in the antecedents of the rules. Improvements to the data structure and the algorithms of the DBCond method are described. An advantage of DBCond is that it is fully parallelizable, thus making it attractive for parallel computing environments.>