The world astronomical image archives offer huge opportunities to time-domain astronomy sciences and other hot topics such as space defense, and astronomical observatories should improve this wealth and make it more accessible in the big data era. In 2010 we introduced the Mega-Archive database and the Mega-Precovery server for data mining images serendipitously containing Solar system bodies, with focus on near Earth asteroids (NEAs). This paper presents the improvements and introduces some new related data mining tools developed during the last years. Currently, Mega-Archive indexed 15 million images available from six major collections and other instrument archives and surveys. This meta-data index collection is daily updated by a crawler which performs automated query of five major collections. Since 2016, these data mining tools are installed on the new dedicated EURONEAR server, and the database migrated to SQL which supports robust and fast queries. To constrain the area to search for moving or fixed objects in images taken by large mosaic cameras, we built the graphical tools FindCCD and FindCCD for Fixed Objects which overlay the targets across one of seven mosaic cameras, plotting the uncertainty ellipse for poorly observed NEAs. In 2017 we improved Mega-Precovery, which offers now two options for the ephemerides and three options for the input (objects defined by designation, orbit or observations). Additionally, we developed Mega-Archive for Fixed Objects (MASFO) and Mega-Archive Search for Double Stars (MASDS). We include a few use case scenarios and we compare our data mining tools with other few similar services. The huge potential of science imaging archives is still insufficiently exploited. Their use could be strongly enhanced by defining a standard format needed to index the image archives. We recommend to the IAU to define such a standard, asking the observatories to index their image archives in a homogeneous manner.

世界天文学影像档案馆为时域天文学科学和其他热门话题（例如太空防御）提供了巨大的机会，天文观测台应改善这一财富，并使其在大数据时代变得更加可访问。在2010年，我们推出了Mega-Archive数据库和Mega-Precovery服务器，用于数据挖掘意外包含太阳系物体的图像的数据采集，重点是近地小行星（NEA）。本文介绍了这些改进，并介绍了最近几年开发的一些新的相关数据挖掘工具。目前，Mega-Archive索引了来自六个主要馆藏以及其他仪器档案和调查的1500万张图像。搜寻器每天都会更新此元数据索引集合，该搜寻器会自动查询五个主要集合。自2016年以来，这些数据挖掘工具已安装在新的专用EURONEAR服务器上，并且数据库已迁移到支持鲁棒和快速查询的SQL。为了限制该区域在大型马赛克相机拍摄的图像中搜索运动或固定对象，我们构建了图形工具FindCCD和FindCCD for Fixed Objects，将目标覆盖在七个马赛克相机之一上，绘制了观察不到的NEA的不确定性椭圆。2017年，我们改进了Mega-Precovery，现在为星历表提供两个选项，为输入（通过名称，轨道或观测值定义的对象）提供三个选项。此外，我们开发了针对固定物体的大型存档（MASFO）和针对双星的大型存档搜索（MASDS）。我们包括一些用例场景，并将我们的数据挖掘工具与其他一些类似服务进行了比较。科学影像档案的巨大潜力仍未得到充分利用。通过定义索引图像档案文件所需的标准格式，可以大大增强它们的使用。我们建议IAU定义这样的标准，要求天文台以统一的方式索引其图像档案。