Improving Knowledge Availability of Forensic Intelligence through Forensic Pattern Warehouse (FPW)

Improving Knowledge Availability of Forensic Intelligence through Forensic Pattern Warehouse (FPW)

Vivek Tiwari (Maulana Azad National Institute of Technology, India) and R. S. Thakur (Maulana Azad National Institute of Technology, India)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch126
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The forensic examination and analysis of the forensic digital data is a pivot point of any investigation. Nowadays, most of the research concentrating only examination of digital data in particular case wise. It will help only find out and get proof against criminals. Nowadays, forensic data are huge and dynamic. As a result a lot of different and complex patterns are extracted. Thus, more elaborate techniques are required in order to extract the hidden knowledge and make these data valuable to the end-users (Manolis & Panos, 2003). Data mining was developed to help to extract Knowledge from the raw data, using algorithms that could discover several statistic properties in the original data (Romero & Abelló, 2010). In order to someone to be able to exploit these patterns, an efficient and general Forensic Pattern Warehouse Management System (FPWMS) is required for handling (storing / processing / retrieving) patterns. Pattern is interesting because it describes a recurrent behavior (Schneider & Wilke, 2012). In conventional system, forensic digital data are stored in some undedicated system. This system is lacking behind when patterns are required for forecasting, prediction and decision making. At this time, we need to perform analysis (apply some data mining or knowledge retrieval methods). This is very time and resource consuming. The main focus behind this chapter is design a dedicated system which is able to store forensic digital data patterns permanently. System consist results or knowledge which is already an analysis of forensic data. In other words, we can say that system gives the ability to retrieve ‘ready to use’ knowledge or patterns. There is introduced forensic pattern warehouse (FPW) concept. There are various data mining techniques which work on forensic digital data to get the patterns. These forensic digital data stored in data warehouse. The downside of data mining techniques is on-demand analysis. This means, at the moments we need knowledge; need to initiate analysis on forensic digital data warehouse and then patterns get lost when system out of memory. This is time consuming and not feasible always. There is no way to store the forensic knowledge (forensic pattern) permanently till date. The proposed introduced concept “pattern warehouse” enables to store forensic patterns permanently. This gives the knowledge on-demand in understandable format. Forensic Pattern Warehouse (FPW) allows to access patterns on-demand. A forensic pattern warehouse (FPW) enhanced forensic intelligence through better forensic data quality, consistency and availability. Government and investigation agencies can obtain various kinds of trend reports e.g. the crime with the most general, rare in a particular area / country for the given period of time etc. The concept of pattern warehouse itself very new and little emphasis has been given to it till date. This chapter will discuss issues, challenges and pyramid architecture for developing forensic pattern warehouse for betterment of forensic prediction and forecasting.

Many government and private forensic databases can help to both law enforcement investigators and the scientists who support their work in the lab. Forensic Pattern Warehouse (FPW) is a centralized forensic data repository that integrates forensic data from various transactional, legacy, applications and external sources. The Forensic Pattern warehouse provides an environment that is separate from the operational systems and is completely designed for decision-support, analytical-reporting, ad-hoc queries, and data mining. This isolation and optimization enables queries to be performed without any impact on the daily transactional and operational systems (Tiwari & Thakur, 2014). Figure 1 depicted various data sources of forensic pattern warehouse. Benefits with a successful implementation of a forensic pattern warehouse include:

Figure 1.

Data sources of forensic pattern warehouse


Key Terms in this Chapter

Pattern: A compact and rich in semantic representation of raw data.

Data Acquisition: A processes used to collect information to document or analyze some phenomenon.

Forensic Data Analysis (FDA): The process of using of controlled and documented analytical and investigative techniques to identify, collect, examine, and preserve digital information.

Data Mining: The practice of analyzing, examining data from different perspectives and summarizing to generate new information.

Pattern-Warehouse: A collection of persistently stored patterns.

Business Intelligence: The process, technologies, and tools needed to turn data, patterns into information, information into knowledge, and knowledge into plans that help for forensic decisions.

Heterogeneous Pattern: Pattern with different structure.

Pattern Warehouse Management System (PWMS): Pattern management system used to model, store, retrieve, and manipulate patterns in an efficient and effective way.

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