Real-time data reduction pipeline for the Korea Microlensing Telescope Network (KMTNet) was developed by Korea Astronomy and Space Science Institute (KASI). The main goal of the data reduction pipeline is to find variable objects and to record their light variation from the large amount of observation data of about 200 GB per night per site. To achieve the goal we adopt three strategic implementations: precision pointing of telescope using the cross correlation correction for target fields, realtime data transferring using kernel-level file handling and high speed network, and segment data processing architecture using the Sun-Grid engine. We tested performance of the pipeline using simulated data which represent the similar circumstance to CTIO (Cerro Tololo Inter-American Observatory), and we have found that it takes about eight hours for whole processing of one-night data. Therefore we conclude that the pipeline works without problem in real-time if the network speed is high enough, e.g., as high as in CTIO.

We have designed data processing server system to include data archiving, photometric processing and light curve analysis for KMTNet (Korea Microlensing Telescope Network). Outputs of each process are reported to the main photometric database, which manages the whole processing steps and archives the photometric results. The database is developed using ORACLE 11g Release 2 engine. It allows to select objects applying any set of criteria such as RA/DEC coordinate and Star ID, etc. We tested the performance of the database using the OGLE photometric data. The searching time for querying 70,000,000 records was under 1 second. The database is fully accessed using query forms via web page.