Map/reduce　is　a　popular　parallel　processing　framework　for　massive-scale　data-intensive　computing.　The　data-iterative　application　is　composed　of　a　serials　of　map/reduce　jobs　and　need　to　repeatedly　process　some　data　files　among　these　jobs.　The　existing　implementation　of　map/reduce　framework　focus　on　perform　data　processing　in　a　single　pass　with　one　map/reduce　job　and　do　not　directly　support　the　data-iterative　applications,　particularly　in　term　of　the　explicit　specification　of　the　repeatedly　processed　data　among　jobs.　In　this　paper,　we　propose　an　extended　version　of　Hadoop　map/reduce　framework　called　Dacoop.　Dacoop　extends　Map/Reduce　programming　interface　to　specify　the　repeatedly　processed　data,　introduces　the　shared　memorybased　data　cache　mechanism　to　cache　the　data　since　its　first　access,　and　adopts　the　caching-aware　task　scheduling　so　that　the　cached　data　can　be　shared　among　the　map/reduce　jobs　of　data-iterative　applications.　We　evaluate　Dacoop　on　two　typical　data-iterative　applications:　k-means　clustering　and　the　domain　rule　reasoning　in　sementic　web,　with　real　and　synthetic　datasets.　Experimental　results　show　that　the　data-iterative　applications　can　gain　better　performance　on　Dacoop　than　that　on　Hadoop.　The　turnaround　time　of　a　data-iterative　application　can　be　reduced　by　the　maximum　of　15.1%.　©　2011　IEEE.