Motivation. Many C++ libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting, and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone. Results. We present Machine Boss, a software tool implementing not just inference and parameterfitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line "recipes" for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage, and nanopore sequence analysis. Availability and Implementation. Machine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/.

A search engine is able to efficiently support novel forms of spatial queries which are integrated with keyword explore. The accessible solutions to such queries also acquire prohibitive space consumption or are unable to give real time answers. As today's need is smart search from search engine not just what they query but relevant to query and similar to that query and where they actually find that product, place or person in real world. To provide such smart search results fast nearest neighbor search with keywords technique by using spatial inverted index (SI-index) has been proposed by researchers. This technique has great efficiency to provide results but processes huge data to fulfill queries. To overcome huge data pre-processing, proposed algorithm reduces no of objects processed by this technique to minimize the memory and processing cost. Proposed technique uses limits to fetch limited objects from the dataset or database. Experiments show great improvement of AFNNSK over FNNSK.

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