Databases Reference
In-Depth Information
1.7.4
Diversity of Database Types
The wide diversity of database types brings about challenges to data mining. These
include
Handling complex types of data:
Diverse applications generate a wide spectrum of
new data types, from structured data such as relational and data warehouse data to
semi-structured and unstructured data; from stable data repositories to dynamic data
streams; from simple data objects to temporal data, biological sequences, sensor data,
spatial data, hypertext data, multimedia data, software program code, Web data, and
social network data. It is unrealistic to expect one data mining system to mine all
kinds of data, given the diversity of data types and the different goals of data mining.
Domain- or application-dedicated data mining systems are being constructed for in-
depth mining of specific kinds of data. The construction of effective and efficient
data mining tools for diverse applications remains a challenging and active area of
research.
Mining dynamic, networked, and global data repositories:
Multiple sources of data
are connected by the Internet and various kinds of networks, forming gigantic, dis-
tributed, and heterogeneous global information systems and networks. The discovery
of knowledge from different sources of structured, semi-structured, or unstructured
yet interconnected data with diverse data semantics poses great challenges to data
mining. Mining such gigantic, interconnected information networks may help dis-
close many more patterns and knowledge in heterogeneous data sets than can be dis-
covered from a small set of isolated data repositories. Web mining, multisource data
mining, and information network mining have become challenging and fast-evolving
data mining fields.
1.7.5
Data Mining and Society
How does data mining impact society? What steps can data mining take to preserve the
privacy of individuals? Do we use data mining in our daily lives without even knowing
that we do? These questions raise the following issues:
Social impacts of data mining:
With data mining penetrating our everyday lives, it is
important to study the impact of data mining on society. How can we use data mining
technology to benefit society? How can we guard against its misuse? The improper
disclosure or use of data and the potential violation of individual privacy and data
protection rights are areas of concern that need to be addressed.
Privacy-preserving data mining:
Data mining will help scientific discovery, business
management, economy recovery, and security protection (e.g., the real-time dis-
covery of intruders and cyberattacks). However, it poses the risk of disclosing an
individual's personal information. Studies on privacy-preserving data publishing and
data mining are ongoing. The philosophy is to observe data sensitivity and preserve
people's privacy while performing successful data mining.
