From tabular data to knowledge graphs: A survey of semantic table interpretation tasks and methods

Tabular data often refers to data that is organized in a table with rows and columns. We observe that this data format is widely used on the Web and within enterprise data repositories. Tables potentially contain rich semantic information that still needs to be interpreted. The process of extracting meaningful information out of tabular data with respect to a semantic artefact, such as an ontology or a knowledge graph, is often referred to as Semantic Table Interpretation (STI) or Semantic Table Annotation. In this survey paper, we aim to provide a comprehensive and up-to-date state-of-the-art review of the different tasks and methods that have been proposed so far to perform STI. First, we propose a new categorization that reflects the heterogeneity of table types that one can encounter, revealing different challenges that need to be addressed. Next, we define five major sub-tasks that STI deals with even if the literature has mostly focused on three sub-tasks so far. We review and group the many approaches that have been proposed into three macro families and we discuss their performance and limitations with respect to the various datasets and benchmarks proposed by the community. Finally, we detail what are the remaining scientific barriers to be able to truly automatically interpret any type of tables that can be found in the wild Web.     

High speed decoding of the binary (47, 24, 11) quadratic residue code

An efficient table lookup decoding algorithm (TLDA) is presented to decode up to five possible errors in a binary systematic (47, 24, 11) quadratic residue (QR) code. The main idea of the TLDA is based on the weight of syndrome, the syndrome decoder together with a reduced-size lookup table (RSLT), and the shift-search method given by Reed et al. Thus, the size of the lookup table and computational complexity in a finite field can be significantly reduced. The memory size of the proposed condensed lookup table (CLT) consists of only 36.6 Kbytes and is only about 0.24% of the full lookup table (FLT) and 3.2% of the lookup up table given by Chen et al., respectively. These facts lead to significant reduction of computational time and the decoding complexity. A simulation result shows that the decoding speed of the proposed TLDA is much faster than all existing decoding algorithms. Moreover, it can be extended to decode all QR codes, including the class of the cyclic codes when the code length is moderate. The CLT makes this new decoding algorithm suitable for hardware or firmware implementations.     

网格环境下的电子病历整合设计

不同电子病历在数据上的异构性阻碍了信息的共享,本文采用基于OSGA-DAI的中间件法解决异构医疗数据的集成问题。OGSA-DAI通过Web Service的方式对数据源进行服务化的封装,屏蔽了数据资源在存储管理上的异构。对于数据资源在信息表示上的异构性,则采用信息映射的方法,将原数据库中的数据映射到一张虚拟的表中,使用户不必了解原数据资源的表示信息即可查询获取数据。     

基于MIDAS的软件远程注册原理及其实现

为了保护软件开发者的利益,可对软件进行远程注册;注册方法是:把注册信息放在本机的一张表中,程序启动之后检查表中的注册信息,若已注册则程序可正常运行;否则,客户必须输入序列号并通过网络在远程服务器上注册。注册成功,在服务器上做上相应的标记。笔者使用基于MIDAS的原理已成功实现了软件的远程注册。     

一种懒惰的Chord指向表更新算法

在节点加入退出或者失效情况下,结构化P2P算法Chord搜索指向表(FT)出现大量指向错误,降低搜索效率。针对该问题,讨论和比较几种搜索 FT的更新策略,分析各算法维护指向正确的开销,提出一种懒惰算法解决搜索 FT更新效率低下的问题。该算法最小化搜索FT更新的消耗,可作为一种有效的错误恢复机制。通过实验对比证明了该算法的有效性。     

片上多核网络存取控制器的设计与实现

提出一种基于片上网络消息传输的高效多核网络存取控制器。该网络存取控制器支持收发双工模式,内置一系列可配置寄存器,采用基于消息表的数据接收方式,通过记录并自动更新不同消息的接收配置信息使数据传输更加高效。使用SMIC 0.18 μm工艺进行综合,结果表明,其工作频率可达300 MHz,规模约为20 443门。     

一种缓冲区溢出攻击的实时检测方法

根据攻击者通常通过修改函数返回地址或函数入口地址来改变程序流程的特点以及ELF文件的结构特点,在调用函数和函数调用返回时对某些特定信息进行处理,以检测出攻击行为。依靠动态程序监控平台pin提供的API函数来编写程序运行时监控工具,提出缓冲区溢出攻击实时检测的方法。实例分析表明该方法具有无需对现有的软、硬件系统进行修改的特点。     

安全结构化对等网络的节点信誉管理机制

提出一种安全结构化对等网络的节点信誉管理机制。使用全局储存方式保存信誉度信息,将文件信誉与节点信誉相结合,避免恶意节点通过修改标识符伪装友好节点的行为。对文件共享系统中的基本操作过程进行分析。在各种节点行为下进行模拟实验,结果证明了该信誉管理机制的有效性。     

Data reductions, fixed parameter tractability, and random weighted d-CNF satisfiability

Data reduction is a key technique in the study of fixed parameter algorithms. In the AI literature, pruning techniques based on simple and efficient-to-implement reduction rules also play a crucial role in the success of many industrial-strength solvers. Understanding the effectiveness and the applicability of data reduction as a technique for designing heuristics for intractable problems has been one of the main motivations in studying the phase transition of randomly-generated instances of NP-complete problems.In this paper, we take the initiative to study the power of data reductions in the context of random instances of a generic intractable parameterized problem, the weighted d-CNF satisfiability problem. We propose a non-trivial random model for the problem and study the probabilistic behavior of the random instances from the model. We design an algorithm based on data reduction and other algorithmic techniques and prove that the algorithm solves the random instances with high probability and in fixed-parameter polynomial time O(dknm) where n is the number of variables, m is the number of clauses, and k is the fixed parameter. We establish the exact threshold of the phase transition of the solution probability and show that in some region of the problem space, unsatisfiable random instances of the problem have parametric resolution proof of fixed-parameter polynomial size. Also discussed is a more general random model and the generalization of the results to the model.     

An order-clique-based approach for mining maximal co-locations

Most algorithms for mining spatial co-locations adopt an Apriori-like approach to generate size-k prevalence co-locations after size-(k − 1) prevalence co-locations. However, generating and storing the co-locations and table instances is costly. A novel order-clique-based approach for mining maximal co-locations is proposed in this paper. The efficiency of the approach is achieved by two techniques: (1) the spatial neighbor relationships and the size-2 prevalence co-locations are compressed into extended prefix-tree structures, which allows the order-clique-based approach to mine candidate maximal co-locations and co-location instances; and (2) the co-location instances do not need to be stored after computing some characteristics of the corresponding co-location, which significantly reduces the execution time and space required for mining maximal co-locations. The performance study shows that the new method is efficient for mining both long and short co-location patterns, and is faster than some other methods (in particular the join-based method and the join-less method).