This paper describes a new programming learning system. It consists of a Web-based flowchart application and Light Emitting Diode (LED) display kits. The LED kits produce various animations or static images according to a flow chart. The flowchart tool allows students to manipulate program elements through a Graphical User Interface (GUI). Survey results show that the proposed system makes learning how to program less challenging and more interesting for beginners. Encouraged by these initial findings, the authors are developing a system with add-on features including sensor accessory options and compatibility with well known LEGO® products.

Time series data, which are a series of one dimensional real numbers, have been studied in various database applications. We extend the traditional similarity search methods on time series data to support a multidimensional data sequence, such as a video stream. We investigate the problem of retrieving similar multidimensional data sequences from a large database. To prune irrelevant sequences in a database, we introduce correct and efficient similarity functions. Both data sequences and query sequences are partitioned into subsequences, and each of them is represented by a Minimum Bounding Rectangle (MBR). The query processing is based upon these MBRs, instead of scanning data elements of entire sequences. Our method is designed: (1) to select candidate sequences in a database, and (2) to find the subsequences of a selected sequence, each of which falls under the given threshold. The latter is of special importance in the case of retrieving subsequences from large and complex sequences such as video. By using it, we do not need to browse the whole of the selected video stream, but just browse the sub-streams to find a scene we want. We have performed an extensive experiment on synthetic, as well as real data sequences (a collection of TV news, dramas, and documentary videos) to evaluate our proposed method. The experiment demonstrates that 73-94 percent of irrelevant sequences are pruned using the proposed method, resulting in 16-28 times faster response time compared with that of the sequential search.