Abstract In big data analysis, a large quantity of OLAP and aggregate queries exists, which have much stronger semantic context relationships (e.g. drill down and roll up) than generic SQL queries. Caching query results in memory playing an important role in accelerating data queries. Nevertheless, traditional query caching schemata neither fully utilize the features of OLAP, such as drill down and roll up semantics, nor compress the cached results, as the memory space is limited. In this paper, we propose a succinct, adaptive semantic OLAP caching, where the cache items are the cube lattice equivalence classes with only the bounds in a class stored. With further queries, the bound ranges are extended or expanded, indicating more query-answering ability which is assessed by the proposed covering capacity. The bounds of equivalence classes that more covering capacity are preferentially preserved in caching. We further empower our cache with some inference ability to derive more new data cells without posing extra queries and develop efficient query and update algorithms. The extensive experimental evaluation is conducted on synthetic and real data sets with various parameter settings. Our cache outperforms the common caching like LRU and LFU. Furthermore, it is robust to the non-repeated-pattern queries, still with a 30% hit ratio.

For a magnetic coupling resonant wireless power transfer (MCR-WPT) system, the most challenging design issue is to maintain the reasonable transfer efficiency and the output power, especially over varying transmitting distances. To address this issue, this study proposes an adaptive impedance matching (AIM) system with a fast optimization control algorithm for the MCR-WPT system. By dynamically monitoring system input impedance variations at a given frequency, the matching system can be automatically activated. The matching precision is enabled by combining an amplitude-phase measurement circuit, a Pi-type impedance matching (IM) network, and the local optimization algorithm. Experimental comparisons show a significant improvement in the transfer efficiency. The maximum improvement is over 32.6%. The results also show that the local optimization algorithm helps the MCR-WPT system to effectively overcome the efficiency challenges caused by parasitic parameters, resulting in a maximum improvement of 19.8%. The stability of the output power is also significantly improved, with a 27.8% reduction in oscillation amplitude compared to the scenario without the local optimization algorithm, and a 60.0% reduction compared to the scenario without the IM network. The output power range (1.55 - 2.21 W) could meet the power requirements of most implantable medical devices. Moreover, this system adopts a control algorithm of tracking the theoretically optimal matching solution through Microcontroller Unit (MCU) computation, leading to a notable enhancement in matching accuracy.

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals.