The　growing　demand　for　computing　power　in　many　sectors　of　industry　calls　for　the　utilization　of　multi-core　processors.　In　order　to　take　advantage　of　multi-core　computing　resources,　efficient　and　effective　multi-core　task　scheduling　strategies　are　needed.　Directed　acyclic　graphs　(DAG)　are　commonly　used　to　represent　the　task　execution　dependencies　that　any　pair　of　tasks　with　dependencies　need　to　be　executed　in　the　specified　order.　In　this　paper,　we　investigated　and　improved　the　DAGs　based　task　model　to　consider　the　temporal　property　of　data　dependencies　between　tasks　that　the　second　task　does　not　have　to　wait　until　the　completion　of　the　precedent　task.　Based　on　such　a　model,　we　proposed　two　scheduling　strategies:　static　decomposed　scheduling　(SDS)　and　dynamic　decomposed　scheduling　(DDS).　The　experimental　results　on　both　synthesized　tasks　and　real　industrial　embedded　programs　show　that　SDS　and　DDS　achieved　good　performance　when　compared　with　some　of　the　state-of-the-art　scheduling　strategies.　©　2019　IEEE.