The　problem　of　maximizing　a　normalized　monotone　non-submodular　set　function　subject　to　a　cardinality　constraint　arises　in　the　context　of　extracting　information　from　massive　streaming　data.　In　this　paper,　we　present　four　streaming　algorithms　for　this　problem　by　utilizing　the　concept　of　diminishing-return　ratio.　We　analyze　these　algorithms　to　obtain　the　corresponding　approximation　ratios,　which　generalize　the　previous　results　for　the　submodular　case.　The　numerical　experiments　show　that　our　algorithms　have　better　solution　quality　and　competitive　running　time　when　compared　to　an　existing　algorithm.