The problem of Proximity Searching in Metric Spaces consists in finding the elements of a set which are close to a given query under some similarity criterion. In this paper we present a new methodology to solve this problem, which uses a t-spanner G′(V, E) as the representation of the metric database. A t-spanner is a subgraph G′(V, E) of a graph G(V, A), such that EA and G′ approximates the shortest path costs over G within a precision factor t.
Our key idea is to regard the t-spanner as an approximation to the complete graph of distances among the objects, and to use it as a compact device to simulate the large matrix of distances required by successful search algorithms such as AESA. The t-spanner properties imply that we can use shortest paths over G′ to estimate any distance with bounded-error factor t.
For this sake, several t-spanner construction, updating, and search algorithms are proposed and experimentally evaluated. We show that our technique is competitive against current approaches. For example, in a metric space of documents our search time is only 9% over AESA, yet we need just 4% of its space requirement. Similar results are obtained in other metric spaces.
Finally, we conjecture that the essential metric space property to obtain good t-spanner performance is the existence of clusters of elements, and enough empirical evidence is given to support this claim. This property holds in most real-world metric spaces, so we expect that t-spanners will display good behavior in most practical applications. Furthermore, we show that t-spanners have a great potential for improvements. 相似文献
In this paper, we present an evolutionary model of industry dynamics yielding endogenous business cycles with ‘Keynesian’
features. The model describes an economy composed of firms and consumers/workers. Firms belong to two industries. The first
one performs R&D and produces heterogeneous machine tools. Firms in the second industry invest in new machines and produce
a homogenous consumption good. Consumers sell their labor and fully consume their income. In line with the empirical literature
on investment patterns, we assume that the investment decisions by firms are lumpy and constrained by their financial structures.
Moreover, drawing from behavioral theories of the firm, we assume boundedly rational expectation formation. Simulation results
show that the model is able to deliver self-sustaining patterns of growth characterized by the presence of endogenous business
cycles. The model can also replicate the most important stylized facts concerning micro- and macro-economic dynamics. Indeed,
we find that investment is more volatile than GDP; consumption is less volatile than GDP; investment, consumption and change
in stocks are procyclical and coincident variables; employment is procyclical; unemployment rate is anticyclical; firm size
distributions are skewed but depart from log-normality; firm growth distributions are tent-shaped.
JEL Classifications: C15, C22, C49, E17, E22, E32. 相似文献
Uber used a disruptive business model driven by digital technology to trigger a ride-sharing revolution. The institutional sources of the company’s platform ecosystem architecture were analyzed to explain this revolutionary change.Both an empirical analysis of a co-existing development trajectory with taxis and institutional enablers that helped to create Uber’s platform ecosystem were analyzed.The analysis identified a correspondence with the “two-faced” nature of ICT that nurtures un-captured GDP. This two-faced nature of ICT can be attributed to a virtuous cycle of decline in prices and an increase in the number of trips.We show that this cycle can be attributed to a self-propagating function that plays a vital role in the spinoff from traditional co-evolution to new co-evolution. Furthermore, we use the three mega-trends of ICT advancement, paradigm change and a shift in people’s preferences to explain the secret of Uber’s system success.All these noteworthy elements seem essential to a well-functioning platform ecosystem architecture, not only in transportation but also for other business institutions. 相似文献