Hierarchical search trees

Spectra and chemical structure searches are based on distance and similarity measures as introduced in Section 5.2. Different strategies are known sequential search, search based on inverted lists, and hierarchical search trees. The strategies are explained for search of spectra. 

To speed up the inference procedure, a tree search is performed instead of a linear search while matching an observation to a regular expression. The tree search uses an automatically generated hierarchical search tree. The original archetypes form the leaves of the tree, while inner nodes are created based on the similarity between the activities based on the nine dimensions. 

Descriptions of the simplest data structure entities and explanations of their nature follow in succeeding sections. Basic data structures are stack, queue, and other linear lists multiple-dimension arrays (recursive) lists and trees (including forests and binary trees). Pointer or link simply means computer data constituting a memory location. Level indicates position in a structure that is hierarchical. Link, level, and the elementary structures are almost intuitive concepts. They are fairly easily understood by reference to their names or to real-life situations to which they relate. Evolving computer practice has had two effects. First, the impact of the World Wide Web and Internet browsers has acquainted many computer users with two basic ideas link (pointer) and level. Second, computer specialists have increased their use of advanced data structures. These may be understandable from their names or descriptive properties. Some of these terms are tries, quad-trees (quadtrees, quaternary trees), leftist-trees, 2-3 trees, binary search trees, and heap. While they are less common data structures and unlikely to be part of a first course in the field, they enable algorithmic procedures in applications such as image transmission, geographic data, and library search. 

A sequential search of a spectral library, for example, involves the comparison of every part of a sample spectrum with library spectra, and is suitable only for small libraries. A hierarchical search involves comparing groups (families) of spectra having the same set of key features as the sample spectrum, enabling large libraries classified in a tree-like structure to be searched very efficiently. 

The initiating event task was a detailed and systematic search for accident initiators that fail barriers to radioactive material release using dendograms (hierarchical trees). Dendograms define barriers and their failure modes in terms of J phenomena (e.g., melt,... 

In order to build an MTree model from more than two Feature Trees, the dynamic match-search algorithm can be applied in a hierarchical manner. We developed two efficient heuristics for this task ... 

The Hierarchical Tree Substructure Search (HTSS) system [25-27] generates a single rooted decision tree from database structures by gradually refining classification of... 

Bruck P, Nagy MZ, Kozics S. Substructure search on hierarchical trees. Online Information 87 Proceedings of the 11th International Online Information Meeting, London, Dec 8-10, 1987. Oxford Learned Information, 1987 41-43. 

An interesting data structure, called flexibility tree, can be employed in the search for the docked complex structure and it allows to combine several flexibility techniques [95]. The data structure hierarchically encodes the flexibility of a protein in variables that allow several protein parts to experience different kinds of moves. Flexibility of domains may be described via normal mode or hinge motions, and side chains are allowed to explore rotameric states. Flexibility trees for protein and ligand are implemented in Flip Dock that provides a genetic algorithm to optimize the variables to gain an induced fit complex ]96]. 

In many instances, data are intended to be associated with a natural structural representation. A typical hierarchical structure is that of a classic organization. A more general representation than a hierarchy, or tree, is known as a network. It is often possible to represent a logical data model with a hierarchical data structure. In a hierarchical model, we have a number of nodes that are connected by links. All links are directed to point from child to parent, and the basic operation in a hierarchy is that of searching a tree to find items of value. When a query is posed with a hierarchical database, all branches of the hierarchy are searched and those nodes that meet the conditions posed in the query are noted and then returned to the DBMS system user in the form of a report. 

A rather different approach to substructure searching is used by at least two systems. One of these is the Chemical Information System (CIS) developed by Richard Feldmann and his colleagues at the National Cancer Institute, and its various off-shoots which include the Chemical Databank System (CDS) at the SERC Laboratory in Daresbury. The other is the Hierarchic Tree Substructure Search (HTSS) developed in Hungary. ... 

HTSS is also rather similar, except that in building the hierarchic tree structure, once all the immediate neighbours for the central atom have been processed, what amounts to a relaxation procedure is applied to extend the tree search file, taking... 

Memory Allocation and Algorithms Hierarchical Data Structures Order Simple, Multiple, and Priority Searching and Sorting Techniques Tree Applications... 

In addition to the necessity of defining similarity, one other major choice must be made in developing library searching approaches. This involves the use of sequential search, the use of inverted (sorted) files, employing hashing methods, or the use of hierarchical trees. Each of these methods has its advantages and is suitable in some circumstances. 

CAS = Chemical Abstracts Service CT = connection table HTSS = hierarchical tree substructure search lO = input-output RDBMS = relational database management system SEMA = stereochemically extended Morgan algorithm. 

A technique, called smoothed particle hydrodynamics (SPH) has been introduced to the Al-body problem. This involves the combination of the two methods of conventional few-body integrators for each body with its immediate nearest neighbors and the overall computation of the distant gravitational potential via FFTs. SPH and FFT methods have also been merged with hierarchical tree searching techniques to improve their speed. Computation of many-body effects, while not important for satellite dynamics, which depend primarily on the central field approximation, may be important in the study of asteroid and comet orbital evolution and also applies to the... 

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