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6. Search Technology Part 2

Martin White O'Reilly Media ePub

In this chapter some of the more sophisticated aspects of search technology are described. All search applications will have the technology components described in Chapter5 but few will have all the technologies set out in this chapter. In selecting a search application it is of little value to use this chapter as a check-list, making a short list from those applications having the greatest number of ticks.

The reasons for this are:

Selecting a search application has to be based on user requirements, and it could be that just one of these features correctly implemented will be quite sufficient to meet these requirements.

The more of these features that are implemented the greater the cost of implementation and administration, ease of upgrading may be reduced, and users may need more training and support.

The concept of entity extraction is to be able to use the search application to identify automatically personal names, locations and other terms that can then be used as query terms without the need to manually index these terms. The technical term for this process is named entity extraction and analyses not just an individual word but also a sequence of words to determine index terms that could be of value in responding to queries. When organizations choose English they are also choosing a language with over 1,000,000 words, a result of invasions and the scale of perhaps the British Empire. The result is a language full of synonyms and polysemes. Fortunately words do not appear in isolation (other than in tables and charts!) so an analysis of a sentence will help substantially in determining the meaning of a word. The mathematics of entity extraction is largely based on the mathematics of Markov Models. A Markov Model describes a process as a collection of states and transitions between states, each of which can be given a probability. Although a knowledge of Markov Models, Hidden Markov Models and the Viterbi algorithm are not a requirement for a search support team it does illustrate the extent to which search is based on mathematics. These and many related mathematical models will be used in different ways by each search vendor and will lead to subtle differences in search performance. These can only be assessed through careful testing at a Proof of Concept stage.

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Medium 9781491947180

Five. Appendixes

David Pogue O'Reilly Media ePub

Appendix A

Appendix B

Appendix C

Appendix D

If your computer came with Yosemite already installed on it, you can skip this appendix—for now. But if you’re running an earlier version of the Mac OS and want to savor the Yosemite experience, this appendix describes how to install the new operating system on your Mac.

If you’re new in these parts, you may be in for quite a shock: You can’t buy Yosemite in a box, or on a DVD. You’re supposed to download it from the Mac App Store.

There are some very attractive elements to the download-only system. For example, there’s no copy protection and no serial numbers to type in. There’s no cost to Apple for manufacturing or shipping, which may explain why Yosemite isn’t even $20 or $30, like its predecessors—it’s free.

Now there’s no disc to hunt down later, when you want to install OS X again (onto a new Mac, for example). And when you do need a copy again, you’ll download the latest version—10.10.3 or whatever it is—instead of having to install whatever updates have come out since you got your DVD.

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Hari Mohan Pandey Laxmi Publications PDF






We have worked with the pointers in the earlier chapters of the book where we worked with pointer to int, char, float and double etc. Similar to pointers to built-in data types we can create pointers to object of class. To create a pointer to an object of class demo we write demo *ptr;

Which creates a pointer of type demo class type. Now for an object say d of demo class declared as demo d; we can store address of this object into pointer ptr as : ptr = &d;

Now any data member or function of demo class can be accessed using pointer as ptr->func_name( ); and ptr->data_member;

As the pointer pr contains the address of object d, *ptr denotes object d so we can also write (*ptr).func_name and (*ptr).deat_member;

We can also create objects dynamically and can store the address into pointer as demo*ptr = new demo;

OR demo *ptr; ptr = new demo;

Here, we don�t have any object d as in the earlier case. Object will be referred only by pointer pt.

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Medium 9780596527631

11. Network Intrusion Detection

Andrew Lockhart O'Reilly Media ePub

Hacks 106120

One type of tool thats come to the forefront in network security in recent years is the network intrusion detection system (NIDS). These systems can be deployed on your network and monitor the traffic until they detect suspicious behavior, when they spring into action and notify you of what is going on. They are excellent tools to use in addition to your logs, since a network IDS can often spot an attack before it reaches the intended target or has a chance to end up in your logs.

Currently, there are two main types of NIDS. The first type detects intrusions by monitoring network traffic for specific byte patterns that are similar to known attacks. A NIDS that operates in this manner is known as a signature-based intrusion detection system. The other type of network IDS is a statistical monitor. These systems also monitor the traffic on the network, but instead of looking for a particular pattern or signature, they maintain a statistical history of the packets that pass through the network and report when they see a packet that falls outside of the normal network traffic pattern. NIDSs that employ this method are known as anomaly-based intrusion detection systems.

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Visualization of Mobility-Density Relation in a Modified Percolation Agent-Based Model

Hamid R. Arabnia, Leonidas Deligiannidis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'13 |


Visualization of Mobility-Density Relation in a

Modified Percolation Agent-Based Model

Bruce Paizen, M.Eng., Jay Kraut, Ph.D., Marcia R. Friesen, Ph.D., and Robert (Bob) D. McLeod,

Department of Electrical and Computer Engineering, University of Manitoba

Abstract-- A modified percolation theory model was developed to incorporate agent mobility on the grid. In this agent-based model (ABM), the impact of agent density was found to significantly influence agent mobility. The visual representation software tool developed provides an intuitive understanding of the ABM simulation dynamics and mechanisms. The software tool visually illustrates that there is a relationship between mobility and density that would have to be taken into account for research into connectedness or connectivity (i.e., epizootic modeling) involving percolation models. Visualization is a common method that is often employed in many percolation models and studies as it helps in narrowing down regions of interest that can be followed up on in a more systematic manner.

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