Tree structures provide a popular means to organize the information on the Web. Taxonomies of thematic categories, concept hierarchies, e-commerce product catalogs are examples of such structures. Querying multiple data sources that use tree structures to organize their data is a challenging issue due to name mismatches, structural differences and structural inconsistencies that occur in such s...

The main source for the lecture on trees is Chapter 8, sections 1 to 4 inclusive, of the textbook [5]. CART is described in the lovely book by Breiman, Friedman, Olshen and Stone [1], which contains an extensive discussion of all the main points we touched during the lecture. A machine-learning point of view is contained in Chapter 3 of Mitchell’s book [8], which is a good reference for ID3 and...

In this supplementary material, we provide additional experimental results along with some details about the inference in our model and its nonlinear extension. 1. Experiments In this section, we provide complementary experimental results. 1.1. Optimization of synthetic tree-structured functions In addition to the results presented in the core of the paper, we include simulations with the non-l...

Full-search vector quantization (VQ) provides optimal results only with high memory and computational cost. We describe the computational and memory requirements of treestructured VQ, residual VQ (RVQ), and tree-structured RVQ. We present multiple-rate, adaptive-search implementations of these VQ structures, and simulation results with video sequences. Tree-structured RVQ provides up to 1.5 db ...

Conjunctive queries are the most basic and central class of database queries. With continued growth demands to manage process massive volume different types data, there is little research study conjunctive between relation tree data. In this paper, we Cross-Model Queries (CMCQs) over tree-structured data (XML JSON). To efficiently CMCQs with bounded intermediate results first encode nodes posit...