Download Paper Topics - NDSU Computer Science

Paper Topics:
You choose your own paper topic. Below are some areas and discussion of important topics within those areas, which
are meant to help you get started thinking of and formulating your own RESEARCH topic. Please submit your topic to me
for approval by emailing a title and abstract to [email protected].. It must be a research topic (makes some new
contribution to the body of knowledge, as opposed to simply an exposition of what has already developed by others).
Only one person per topic (first come first serve - email your request to me). Your paper should be high quality in terms
of style and correctness. This research project is YOUR project. I am suggesting topics but if you choose one of my
suggested topics, that makes it your topic. You should choose one of my suggestions only if you understand it and think
it has potential as a research topic for you. The suggestions are meant to help you find a suitable topic but are not
intended to limit you to these topics.
Be sure to include:
INTRODUCTION AND CONTEXT. Research the "area" of the topic (put it in context of what has been done by others,
what is still left to do, what you are contributing. - as to what has been written by others, see our text, ACM Computing
Reviews, online database searches... - be sure to include a paper which was written since 1990 so that you will have
some assurance that you are tied in with the latest round of research on the topic). This usually forms the Introduction
section of your paper and should be about 300-2000 words in length or longer.
MAIN NEW CONTRIBUTION (KILLER IDEA). Detail your contribution so that it can be followed by a reader who is new to
area. (this can be an expansion of the "what you are contributing section of your Introduction). It would be best to have
just one killer idea and do it well. This section should be about 800-3000 words in length or longer.
PROOF. Prove that your idea is correct and makes the contribution you claim it does (i.e., it is a "killer" idea). This differs
with topic and area. If the contribution or "killer idea" involves random variations (stochasticity) a simulation may be
required. If not, an analytic model (assumptions, formulas and analysis of results) may do the job. Actual
experimentation may also be possible, though that involves prototyping the system itself. We can email-talk about this
section on an individual basis. This section should be about 500-3000 words in length or longer.
CONCLUSION. Summarize the most important points and contributions you have made. Note that you will be "telling us
what you're going to do" in the Intro; "doing it" in the Idea and Proof Sections; and then "Telling us what you did" in the
Conclusion Section. Thus, you will say the same thing thrice - in different ways, for different purposes and in different
depth levels. This section should be about 200-1000 words in length or longer.
A topic area, which overlaps with web search querying and analysis, software engineering reachability graph analysis
and control flow graph analysis, sales graph analysis and bioinformatics interactions analysis; is the need to analyze
multiple interactions for common "strong interaction cells".
Websites interactions: Two websites interact if one contains the other's URL (this is a "directional interaction" and is
modeled by a directed graph in which the nodes of the graph are websites (URLs), and there is a directed edge running
from a URL to each of the URLs it contains on its website). One can simply analyze "existence" of references (an
unlabeled directed edge iff the source URL contains one or more instances of the destination URL), or one can analyze
"strength" of reference (a labeled graph in which the label on any edge records the number of times the destination URL
occurs on the source URL page.
For example, two websites interact iff they are reference on the same webpage (undirected graph which can be labeled
with "strength = # of different pages reference both" or just "existence = unlabeled edge" iff they are co-reference). Two
websites interact iff a given user goes from one site immediately to the other site during a web surfing session (this is
"directional" so it is modeled by a directed graph which can have labeled edges (count of user traces) or just existence
(at least one user trace).
Bioinformatics:
For example, two genes (or proteins) interact iff their expression profiles in a microarray experiment are similar enough
(this would be an unlabeled undirected graph - it could include "strength" = the level of similarity as an undirected edge
label). Two genes (or proteins) interact iff the proteins they code for interact (in some particular way - i.e., occur in the
same pathway; combine into the same complex, etc.). Two genes (or proteins) interact iff they are co-referenced in the
same document in the PubMed literature (again. "existence" or "strength" are possible). Actually, this third point is not
true most of the times. The authors might refer to other genes in different contexts with respect to the genes they are
working on. This doesn't mean that there is an interaction among the genes that were mentioned in the same
document. It is in this scenario, GENE ONTOLOGY (GO) comes into picture. GO has several evidence codes which signify
how the functions of the genes/gene products were assigned. For GO evidence codes, refer
http://www.geneontology.org/GO.evidence.shtml#ic. Before confirming the interaction among the genes in the same
document, it would be good to cross check with the GO evidence codes.
Software Engineering:
For example, two programs interact iff the same code segment occurs in both (undirected, either labeled with a strength
= e.g., the number of times that segment occurs, or existence = just whether the segment co-exists at least once for
not). Two programs interact iff they call the same program. Two programs interact iff they are called by the same
program. Two programs interact iff they contains roughly the same set of constants (variables) with respect to some
ontology or standards listing. Two programs interact iff they have the same aspect designation.
Sales Analysis:
For example, two products interact iff they co-occur at checkout 80% of the time (or with some other threshold support
= "% of market baskets"). Two products interact iff when one occurs in a market basked, 80% of the time, the other will
also (this is the "directed graph" version of 1 above and has to do with the "confidence" of the association). Two
products interact iff the same salesman sells both. Two products interact iff they are sold in the same region (at a
threshold level, or as a labeled graph, label that edge with the number sold). Two products interact iff they are sold at a
threshold level during the same season (e.g., in December).
Security Applications:
For example, two persons interact iff they are from the same neighborhood (or city or state or country). Two persons
interact iff they are in the same occupation. Two persons interact iff they have similar records (employment records,
criminal records, etc.). Two persons interact iff they belong to the same organizations. Two events interact iff they are
attended by similar sets of attendees. Two locations interact iff they are visited by similar sets of people. Two locations
interact iff they are associated with similar events. In all the interactions, the graph model is central and one is looking
for strong clusters of nodes (nodes that are strongly associated with via the edge set) How do we find the strong
clusters? What do we mean precisely by a cluster?
Notes:
Using vertical technologies to search out common clusters or quasi-clusters or "cliques" should be very valuable in
bioinformatics as well as in web analysis. For instance, there are thousands of interaction graphs of interest over a given
set of genes (proteins). Using vertical technology, it is possible to construct an index attribute and an order attribute for
each interaction graph and to analyze them (using Dr. Daxin Jiang's methods or other methods - e.g., OPTICS-like)
directly. You can find Dr. Daxin Jiang's work here: Jiang_TKDE_paper A shorter version (preliminary work): Jiang_paper
You can get the OPTICS paper to get some understanding about ordering-based algorithm here: OPTICS paper For each
data set (either a micro-array dataset or an interaction graph data set), construct two derived attributes, the step count
attribute from the ordering and the index attribute. For each pair of such added attributes, we can quickly search for the
pulses using vertical technology (just a matter of looking for those genes where the index exceeds a threshold and move
that threshold down until the user feels he/she has found the appropriate pulses. These "pulse genes" have a step
number in the ordering. For each pulse gene, we can quickly extract the forward subinterval from that pulse to the next.
Each such search will give us the "flat region", from which the strong cluster associated with that pulse can be extracted.
So we will have a vertical "mask" defining each strong cluster from each dataset. We can quickly "AND" those to find
common strong clusters using vertical technology. With this minor extension to Dr. Daxin Jiang's wonderful tool and a
re-coding of the tool for vertical data, one could analyze across multiple interaction graphs. I think that is the main
exciting application and across multiple micro-arrays (may be?) and across multiple web graphs. When the dataset is
very large, the scalability becomes a very important issue.
A new method of classification or clustering based on Derived Attributes that are "Walk-based". The walks can be based
on Z-ordering, Hilbert ordering or another ordering (or random walks?).
An new method of classification or clustering based on some statistic derived from the Covariance matrix of a training
space or space to be clustered.
A new method of classification or clustering based on some combination of derived attributes that are variation based
and walk based.
An automatic alerter system to be used by Software Engineers which will automatically alert the development team
when some type of "bad situation" or "dangerous practice" is detected (e.g., within a system such as CVS for storing
"development version", when a version is "checked in", the alerter analyzer would immediately analyze (classify based
on the database of past development projects done with the CVS system?) for the "exceptional situation.
Based on the idea in Automatic alerter used by Software Engineers which will automatically alert the development team
when some type of "bad situation" or "dangerous practice" is detected (e.g., within a system such as CVS for storing
"development version", when a version is "checked in", the alerter analyzer would immediately analyze (classify based
on the database of past development projects done with the CVS system?) for the "exceptional situation.) develop a
specific alerter for an ASPECT.
Based on the idea relaed to Automatic alerter used by Software Engineers which will automatically alert the
development team when some type of "bad situation" or "dangerous practice" is detected (e.g., within a system such as
CVS for storing "development version", when a version is "checked in", the alerter analyzer would immediately analyze
(classify based on the database of past development projects done with the CVS system?) for the "exceptional situation.)
develop a specific alerter for "Development shop coding rule violations".
Based on the idea in Automatic alerter used by Software Engineers which will automatically alert the development team
when some type of "bad situation" or "dangerous practice" is detected (e.g., within a system such as CVS for storing
"development version", when a version is "checked in", the alerter analyzer would immediately analyze (classify based
on the database of past development projects done with the CVS system?) for the "exceptional situation.) develop a
specific alerter for "Standards Organization standards violations".
Based on the idea in Automatic alerter used by Software Engineers which will automatically alert the development team
when some type of "bad situation" or "dangerous practice" is detected (e.g., within a system such as CVS for storing
"development version", when a version is "checked in", the alerter analyzer would immediately analyze (classify based
on the database of past development projects done with the CVS system?) for the "exceptional situation.) develop a
specific alerter for "highly risky situations".
Based on the idea in Automatic alerter used by Software Engineers which will automatically alert the development team
when some type of "bad situation" or "dangerous practice" is detected (e.g., within a system such as CVS for storing
"development version", when a version is "checked in", the alerter analyzer would immediately analyze (classify based
on the database of past development projects done with the CVS system?) for the "exceptional situation.) develop a
specific alerter for "potential security problems (code that invites hackers to hack)".
New Method of Cluster Data Mining Support that DBMSs Should Use (with reasons). How do Oracle, IBM DB-2,
Microsoft SQL Server support clustering and how would you improve on these methods?
Decision Tree Induction Classification Implementation and Performance Analysis for Numeric Data. Implement a new
method of Decision Tree Induction classification data mining. Prove that your method performs well compared to ID3
C4.5, C5 or other known methods for at least one type of data.
Decision Tree Induction Classification Implementation and Performance Analysis for Categorical Data. Implement a new
method of C4.5 or C5 -like decision tree induction classification data mining method and prove it compares well to C4.5
or C5 or other known methods for categorical data.
Bayesian Classification Implementation and Performance Analysis. Implement a new method of Bayesian classification
data mining and prove it compares well to known methods.
Neural Network Classification Implementation and Performance Analysis. Implement a new method of Neural Network
classification data mining and prove it compares well to known methods (how well does it scale to large datasets?)
K-Nearest Neighbor Classification Implementation and Performance Analysis or K-Most Similar Classification
Implementation and Performance Analysis. Implement a new method and prove it compares well to known methods.
Density-Based Classification Implementation and Performance Analysis. Implement a new method of Density-Based
classification and prove it compares well to known methods.
Genetic-Algorithm-Based Data Mining Implementation and Performance Analysis. Implement a new method of GeneticAlgorithm-Based classification and prove it compares well to known methods.
Simulated Annealing-Based Classification Implementation and Performance Analysis. Implement a new method of
Simulated-Annealing-Based classification and prove it compares well to known methods.
Tabu-Search-Based Classification Implementation and Performance Analysis. Implement a new method of Tabu-SearchBased classification and prove it compares well to known methods.
Rough-Set-Based Classification Implementation and Performance Analysis. Implement a new method of Rough-SetBased classification and prove it compares well to known methods.
Fuzzy-Set-Based Classification Implementation and Performance Analysis. Implement a new method of Fuzzy-Set-Based
classification and prove it compares well to known methods.
Markov-Modeling-based Classification and Performance Analysis. (Hidden Markov Model based, ...) Implement a new
method of Markov-Chain-Based classification and prove it compares well to known methods. Reference "Evaluation of
Techniques for Classifying Biological Sequences", Deshpande and Karypis, PA-KDD Conf. 2002, Springer-Verlag Lecture
Notes in Artificial Intelligence 2336, pg 417.
Multiple-Regression Data Mining Implementation and Performance Analysis. Implement a new method of multipleregression-based data mining and prove it compares well to known methods.
Non-linear-Regression Data Mining Implementation and Performance Analysis. Implement a new method of non-linearregression-based data mining and prove it compares well to known methods.
Poisson-Regression Data Mining Implementation and Performance Analysis. Implement a new method of Poissonregression-based data mining and prove it compares well to known methods.
Association Rule Mining Implementation and Performance Analysis. Implement a new method of Association Rule
Mining and prove it compares well to known methods (e.g., Frequent Pattern Trees).
Multilevel Association Rule Mining Implementation and Performance Analysis. Implement a new method of Multilevel
Association Rule Mining and prove it compares well to known methods.
Counts-count Association Rule Mining Implementation and Performance Analysis. Implement a new method of Countscount Association Rule Mining and prove it compares well to known methods. Counts count ARM means that the
method takes account of the number of each item in a market basket, not just whether or not the item is bought (1 or
more).
Partitioned Hash Functions. Devise a variation on the basic Partitioned Hash Function structure which you can show will
perform in a superior way in some particular multidimensional setting and workload.
Multi-key Index. Devise a variation on the basic Multi-key Index structure which you can show will perform in a superior
way in some particular multidimensional setting and workload.
kd-Trees: Devise a variation on the basic kd-Tree index structure which you can show will perform in a superior way in
some particular multidimensional setting and workload. Apply #24 kd-tree to the computer graphics (detailed speaking:
ray shooting), that is, developing and improving kd-tree to get good performance for scenes of different complexities.
R-Trees: Devise a variation on the basic R-Tree index structure which you can show will perform in a superior way in
some particular multidimensional setting and workload.
K-Means Clustering. Implement a new method of K-Means Clustering and prove it compares well to known methods.
K-Medoids Clustering. Implement a new method of K-Medoids Clustering and prove it compares well to known methods.
K-Nearest Neighbor Clustering. Implement a new method of K-Nearest Neighbor Clustering and prove it compares well
to known methods. A reference is "Clustering Using a Similarity Measure Based on Shared Near Neighbors", Jarvis and
Patrick, IEEE Transactions on Computers, Vol. c-22, No. 11, November 1973.
Agglomerative Hierarchical Clustering. Implement a new method of Agglomerative Hierarchical Clustering and prove it
compares well to known methods such as AGNES.
Divisive Hierarchical Clustering. Implement a new method of Divisive Hierarchical Clustering and prove it compares well
to known methods such as DIANA.
Hierarchical clustering similar to BIRCH Implement a new method similar to BIRCH clustering and prove it compares well
to known methods such as BIRCH itself.
Clustering similar to CURE. Implement a new method similar to CURE clustering and prove it compares well to known
methods such as CURE itself.
Clustering similar to OPTICS. Implement a new method similar to OPTICS clustering and prove it compares well to known
methods such as OPTICS itself.
Clustering similar to DB-SCAN. Implement a new method similar to DB-SCAN clustering and prove it compares well to
known methods such as DB-SCAN itself.
Grid-based clustering similar to STING. Implement a new method similar to STING clustering and prove it compares well
to known methods such as STING itself. Grid-based clustering similar to CLIQUE. Implement a new method similar to
CLIQUE clustering and prove it compares well to known methods such as CLIQUE itself.
CLARANS partitioning clustering. Implement a new clustering method similar to the CLARANS and prove it compares well
to known methods such as CLARANS itself.
Hierarchical clustering similar to ROCK Implement a new method similar to ROCK clustering and prove it compares well
to known methods such as ROCK itself.
Hierarchical clustering similar to CAMELEON Implement a new method similar to CAMELEON clustering and prove it
compares well to known methods such as CAMELEON itself.
Density-based clustering similar to DENCLUE. Implement a new method similar to DENCLUE clustering and prove it
compares well to known methods such as DENCLUE itself.
Statistics-based clustering similar to COBWEB. Implement a new method similar to COBWEB clustering and prove it
compares well to known methods such as COBWEB itself.
Statistics-based clustering similar to CLASSIT. Implement a new method similar to CLASSIT clustering and prove it
compares well to known methods such as CLASSIT itself.
Statistics-based clustering similar to AutoClass. Implement a new method similar to AutoClass clustering and prove it
compares well to known methods such as AtuoClass itself.
The ACID Properties Testing & Performance------- Devise an approach to test the support of the ACID properties which
are enforced by the concurrency control and recovery methods for various DBMSs you have access to.
Concurrency control using ROLL and ROCC and MVROCC -like methods for heterogeneous distributed database systems.
Work out a serializable concurrency control scheme based on the ROLL protocol which could be implemented on a
variety of system which are autonomously running a off-the-shelf database system.
ROLL Concurrency Control with Space Efficient Request Vectors. Develop and test a ROLL Concurrency Control method in
which the Request Vectors are compressed (such as with Ptrees).
New Deadlock Management Method. Especially for widely distributed data on a distributed DBMS devise and test a new
deadlock management method. Devise a method particularly well suited for this environment and give some
justification for believing it is superior to existing methods.
Quad-Trees-II (Kinked Quad Trees) : Devise a variation on the basic Quad-Tree index structure which you can show will
perform in a superior way in some particular multidimensional setting and workload. Consider the following. Instead of
quadration using vertical and horizontal division lines replace the horizontal line with a "kinked" line (to joined line
segments) and the vertical line with a "kinked" line, carefully chosen to distribute the points evenly in the quadrants.
The only additional overhead is that the "cut-lines" would be recorded using more parameters than simple quad trees.
Another possibility which might be compared to basic quadtrees and to "kinked" quadtrees is "Poly Quad Trees" in
which a polynomial is used in each case instead of a line (e.g., a parabola, a polynomial of degree 2, degree 3, ...)
BEGIN order Recovery. The RECOVERY PROCESS with check-pointing is as follows: 1. Start at the Checkpoint record in the
LOG. Put the "active list" into an UNDO list. 2. Work forward in the LOG. For every BEGIN encountered, put the
transaction in the UNDO list For every COMMIT encountered move trans from UNDO to REDO list. 3. When the end of
the LOG is reached, Redo all transactions in the REDO list in REDO-list order UNDO all transactions in the UNDO list Note:
Since transactions are being redone in REDO-List order (commit order in this case), it must be the case that the Serial
Order to which execution is equivalent is COMMIT order. In T2 and T4 above, messages may have gone back to the users
which were based on and execution order equivalent to SOME serial order (values reported to users were generated by
execution in that order). Thus, RECOVERY must regenerate the same values. The only way that the RECOVERY process
can know what serial order the original execution was equivalent to is that the initial execution be equivalent to some
serial order identifiable from the LOG. One order identifiable from the LOG is COMMIT order. Therefore, it is common to
demand that the order of execution be equivalent to the serial COMMIT-order (e.g., use Strict 2PL?) Are there other
possibilities? How about "arrival or BEGIN order"? How could that be made determinable from the LOG? How does this
compare to COMMIT order? Better? Worse?
"ITERATIVE DYNAMMIC PROGRAMMING" and its high potential in QUERY OPTIMIZATION.
"The Application of P-tree in the power plant MIS" "K-Clustering using P-trees"
1. Coding statistical issue such as sum, mean, and variance using the DMI;
2. Focus on our k-clustering algorithm 3
. Compare performance with K-mean, mean-splitting, variance-based algorithm.
"Addressing number in MBR ARM Implementation and Performance Analysis."
"SOM Clustering Using P-Trees"
Support Vector Machine -like Classification method.
Develop a process of structuring data into a vertical database? Prove it has some good features. Compare it to other
vertical database structuring processes and/or to horizontal database structuring processes (e.g., ER diagramming...).
Develop some aspect of the query processor for a vertical database. Prove that it has some good features. Try to
compare it either to other vertical query processing approaches (e.g., the ones in the notes) and/or to horizontal
database query processing.
Develop some aspect of the Data Mining processor for a vertical database. Prove that it has some good features. Try to
compare it either to other vertical data mining approaches (e.g., the ones in the notes) and/or to horizontal database
data mining processing.
Wikipedia is a new hyperlinked database. It is developing very rapidly into a major source of online information. Many
topics spring to mind in and around Wikipedia. E.g.,
Analyze the link structure in Wikipedia (related to "analyze interactions" group at the beginning of this "topics" section).
Automated link setting in wikipedia.