A number of emerging applications of data management technology involve the monitoring and querying of large quantities of continuous variables, e.g., the positions of mobile service users, termed moving objects. In such applications, large quantities of state samples obtained via sensors are streamed to a database. Indexes for moving objects must support queries efficiently, but must also support frequent updates. Indexes based on minimum bounding regions (MBRs) such as the R-tree exhibit high concurrency overheads during node splitting, and each individual update is known to be quite costly. This motivates the design of a solution that enables the B § -tree to manage moving objects. We represent moving-object locations as vectors that are timestamped based on their update time. By applying a novel linearization technique to these values, it is possible to index the resulting values using a single B § -tree that partitions values according to their timestamp and otherwise preserves spatial proximity. We develop algorithms for range and¨nearest neighbor queries, as well as continuous queries. The proposal can be grafted into existing database systems cost effectively. An extensive experimental study explores the performance characteristics of the proposal and also shows that it is capable of substantially outperforming the R-tree based TPRtree for both single and concurrent access scenarios.

Using a megastudy approach, we developed a database of lexical variables and lexical decision reaction times and accuracy rates for more than 25,000 traditional Chinese twocharacter compound words. Each word was responded to by about 33 native Cantonese speakers in Hong Kong. This resource provides a valuable adjunct to influential mega-databases, such as the Chinese single-character, English, French, and Dutch Lexicon Projects. Three analyses were conducted to illustrate the potential uses of the database. First, we compared the proportion of variance in lexical decision performance accounted for by six word frequency measures and established that the best predictor was Cai and Brysbaert's (PLoS One, 5, e10729, 2010) contextual diversity subtitle frequency. Second, we ran virtual replications of three previously published lexical decision experiments and found convergence between the original experiments and the present megastudy. Finally, we conducted item-level regression analyses to examine the effects of theoretically important lexical variables in our normative data. This is the first publicly available large-scale repository of behavioral responses pertaining to Chinese two-character compound word processing, which should be of substantial interest to psychologists, linguists, and other researchers.Keywords Chinese . Compound word . Megastudy . Reaction time . Visual word recognition In English, a compound word is typically formed by a combination of two or more constituent words (e.g., snow and man in snowman) (Dressler, 2006). Similarly, in Chinese, compound words are often formed via a combination of two or more characters that are almost always monosyllabic morphemes (e.g., 雪人 snow-man [snowman]). However, about 73.6 % of modern Chinese words are two-character compound words (Institute of Language Teaching and Research, 1986), showing that compounding is normative, rather than exceptional, in Chinese word formation (Packard, 2000). Adopting the megastudy approach in Balota et al. 's (2007) English Lexicon Project, we collected normative data for participants' lexical decision (i.e., deciding whether a twocharacter string forms a Chinese word, e.g., 朋友 [friend], or a nonword, e.g., 形忌) performance for more than 25,000 twocharacter Chinese words varying on various lexical characteristics, such as word frequency. Before elaborating on the details of the present megastudy, we first provide a brief selective review of the literature on Chinese two-character compound word processing, including a discussion of its limitations, and then show how the megastudy approach can complement this body of factorial work and help shed additional light on Chinese compound word processing.Electronic supplementary material The online version of this article

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This paper assumes a setting where a population of objects move continuously in the Euclidean plane. The position of each object, modeled as a linear function from time to points, is assumed known. In this setting, the paper studies the querying for dense regions. In particular, the paper defines a particular type of density query with desirable properties and then proceeds to propose an algorithm for the efficient computation of density queries. While the algorithm may exploit any existing index for the current and near-future positions of moving objects, the B x -tree is used.