Fast Mining of Sequential Patterns in Big Data

min. read

December 24, 2024

Fast Mining of Sequential Patterns in Big Data

Sequential pattern mining in big data uncovers hidden trends in large datasets. Here's what you need to know:

Identifies frequent subsequences in data
Helps make better decisions and predictions
Challenges: data volume, processing speed, pattern complexity
New algorithms tackle these issues:
- Pattern Growth Methods (e.g., METISP)
- Vertical Data Format Techniques (e.g., SPADE)
- Parallel and Distributed Systems (e.g., PNRD-CloGen)

Key improvements:

Co-occurrence maps and time-indexed structures boost efficiency
Constraints like time limits narrow down relevant results
Pruning methods and better support counting speed up processing

Real-world applications:

E-commerce: Predicting user behavior
Marketing: Understanding customer journeys
Retail: Smart inventory management
Biology: Analyzing DNA patterns

Future trends:

AI integration for smarter mining
Edge computing for real-time processing
Ethical considerations in data handling

Industry	Use Case	Impact
Finance	Fraud detection	Real-time prevention
Retail	Customer analysis	Personalized recommendations
Healthcare	Patient data analysis	Improved diagnoses

Bottom line: Sequential pattern mining in big data is faster and more practical than ever, with applications across industries.

Main Hurdles in Big Data Sequential Pattern Mining

Mining sequential patterns in big data is tough. Here are the key challenges:

1. Data Volume Overload

Big data sets are massive. Traditional algorithms can't handle them. SPAMC, for example, had to process 12.8 million transactional sequences. That's a lot!

2. Speed Bumps

Processing speed matters. Old methods often need multiple data processing rounds. This slows things down, especially with big data sets.

3. Pattern Complexity

More data means more possible patterns. This leads to a "combinatorial explosion" - too many subsequences to check.

4. Computing Power Hunger

Big data needs serious computing power. This often means using distributed systems, which aren't simple.

Challenge	Impact	Solution Approach
Data Volume	Overwhelms old algorithms	Use distributed frameworks (e.g., MapReduce)
Processing Speed	Multiple rounds slow mining	Algorithms with fewer processing rounds
Pattern Complexity	Slows computation	Better pruning techniques
Computing Power	Needs distributed systems	Cloud solutions with load balancing

5. Balancing Act

Spreading work evenly across machines is tricky. The SPAMC-UDLT algorithm tries to solve this, but it's still a challenge.

6. Constraint Conundrum

Filtering out boring patterns helps, but it makes mining more complex.

"We often need to tweak existing algorithms with constraints or domain-specific ideas for good results in specific applications." - P. Fournier-Viger, SPMF Founder

7. Iterative Issues

Some algorithms, like SPAMC, use iterative processes. These can cause communication and scheduling problems in distributed setups.

8. Scalability Struggles

As data grows, algorithms must scale well. Many current methods struggle with this.

Solving these issues is crucial for big data sequential pattern mining. It's complex, but the payoff is huge insights across industries.

New Algorithms for Quick Sequential Pattern Mining

Data mining just got a speed boost. New algorithms are tackling big data challenges head-on, using clever tricks and parallel processing.

Pattern Growth Methods

PrefixSpan and its cousins use projected databases to mine faster. But METISP takes it up a notch:

One database scan
Memory indexing for time constraints
Beats GSP and DELISP

A warehouse manager could use METISP to analyze quarterly sales:

Constraint	Value
Minimum support	40%
Minimum gap	7 days
Maximum gap	30 days
Sliding window	2 days
Duration	90 days

Result? Smarter stock prep based on real patterns.

Vertical Data Format Techniques

SPADE and friends flip the script on data representation:

Item id-list pairs
Faster support counting
Better pruning of rare sequences

cSPADE handles time constraints, but still crunches through lots of 2-sequences for max-gap constraints.

Parallel and Distributed Systems

Big data needs big processing power. Enter parallel algorithms:

1. PNRD-CloGen Algorithm

Multi-core mining
Closed + generator patterns
Dynamic bit vector structure

PNRD-CloGen crushed it on real datasets:

Dataset	Sequences	Items	Ave. length	Type
BMSwebview1	59,601	497	2.42	Sparse
Sign	800	267	51.997	Very dense
FIFA	20,450	2,990	34.47	Moderate dense
Korsarak	990,000	41,270	8.1	Large
MSNBC	1,043,878	2,922	13.23	Very large

On the Sign dataset (0.1 min support): PNRD-CloGen 105.6s vs NRD-DBV 514.3s.

2. PTDS (Parallel Transaction-Decomposed Sequential pattern mining)

Turbocharges pattern growth
Tested on 16 million sequences
Beats PrefixSpan-based parallel methods

3. Hash Partitioned Sequential Pattern Mining (HPSPM)

Hash function for candidate partitioning
No data broadcasting
Less comparison work
Tested on IBM SP2 parallel computer

The takeaway? Parallel processing is the key to mining big data fast. These algorithms split the work across cores or machines, crunching massive datasets way quicker than old-school methods.

New Data Structures for Better Mining

Big data mining needs smart tools. Let's look at two that are changing the game:

Co-occurrence Maps

These maps show how often things appear together in data. They help narrow searches and speed things up.

But there's a problem: old-school co-occurrence matrices get huge and slow with big data. That's where Hadoop comes in:

Approach	Benefits
Hadoop Streaming	Scales well, works fast, adapts easily
Map-reduce paradigm	Makes things simpler
Java implementation	Breaks text into words, pairs them up

This setup crunches big data way faster than before.

Time-Indexed Structures

These structures save memory and work faster. The METISP algorithm uses them for time-based pattern mining.

METISP beats older algorithms like GSP and DELISP. Here's why:

Scans data once
Builds time-index sets in memory
Doesn't generate candidates
Doesn't create sub-databases

Let's compare METISP to P-PrefixSpan:

Algorithm	Time (ms)	Memory Usage
METISP	80	0.732
P-PrefixSpan	95	1.162

METISP scales well with bigger data, making it a top pick for big data mining.

These new structures aren't just faster - they're smarter. They use less memory and narrow searches efficiently. For data pros, this means quicker insights and better use of resources.

Mining with Constraints

Constraints in sequential pattern mining help zero in on relevant results. Let's explore two key constraint types:

Time Limits

Time constraints narrow down pattern discovery:

Minimum gap: Shortest time between events
Maximum gap: Longest time between events

These limits find patterns within specific timeframes.

The MoocData platform analyzed 82,535 course enrollment sequences from XuetangX, a major Chinese MOOC platform. Data spanned from October 1, 2016, to March 31, 2018.

By setting min and max gaps, researchers spotted enrollment patterns over time. This helped them grasp learning behaviors and boost course recommendations.

Item Limits and Pattern Weights

These constraints further refine mining results:

Constraint	Description	Benefit
Length	Caps items in a pattern	Focuses on manageable patterns
Discreteness	Measures event time variance	Favors consistent timing
Weight	Assigns item importance	Highlights high-value patterns

SPM-FC-L and SPM-FC-P algorithms use these constraints. They beat traditional methods by shrinking the search space and boosting efficiency.

A MOOC data study showed that flexible constraints produced fewer but more meaningful patterns than standard mining.

"The number of FCSPs was always smaller than FSPs, proving constraints effectively filter results."

This approach shines with large, dense databases and low minimum support thresholds. It generates fewer, more crucial patterns, saving time and resources.

Ways to Improve Performance

Let's look at how to speed up sequential pattern mining in big data. Researchers have come up with some clever tricks to make things faster and use fewer resources.

Pruning Methods

Pruning is all about cutting out the stuff you don't need. It's like trimming a tree to help it grow better. Two key pruning methods are:

Sequence Extension Pruning (SEP)
Item Extension Pruning (IEP)

These work great with certain types of algorithms. There's even a souped-up version called SPAM_SEPIPE that uses both SEP and IEP. It's like a one-two punch for trimming down the search space.

"SPAM_SEPIPE beats SPAM by 10 times on small datasets and 30% to 50% on bigger ones."

Sure, it might use a bit more memory, but the speed boost is worth it.

Better Support Counting

When you're dealing with big data, you need to count things fast. Here's how to do it:

1. Probabilistic Data Structures and Algorithms (PDSA)

These are fancy tools that help you count stuff quickly without using too much memory.

2. HyperLogLog Algorithm

This is a type of PDSA that's pretty accurate and efficient:

Feature	Value
Standard Error	0.81%
Memory Usage	12 KB per data structure

Redis, a popular database, uses HyperLogLog in real-world situations.

3. Weighted Support Affinity Pattern Mining

There's an algorithm called WSMiner that's good at finding patterns without wasting time on stuff that doesn't matter.

Comparing Fast Mining Methods

Fast

Not all algorithms for mining sequential patterns in big data are equal. Let's see how the popular ones stack up.

PrefixSpan often comes out on top. It's faster than GSP, FreeSpan, and SPADE in most cases. And when you add pseudoprojection to PrefixSpan? It's the fastest of the bunch.

Here's a quick look at some key algorithms:

Algorithm	Strengths	Best For
PrefixSpan	Fast, handles constraints well	Large sequence databases
SPADE	Quick on certain datasets	Sparse datasets
FAST	High performance	Sparse datasets
LAPIN	High performance	Dense datasets

Your algorithm choice can make or break processing speed. For example, SPADE was the quickest on the Kosarak10k dataset.

But it's not one-size-fits-all. Different algorithms shine on different datasets. Let's look at three examples:

1. BMSwebview1 dataset

TRuleGrowth beat NRD-DBV in speed, but NRD-DBV used less memory.

2. Kosarak dataset

NRD-DBV cranked out 21 rules in 200 seconds. TRuleGrowth? It couldn't keep up. TNS lagged behind, producing only a quarter of the rules.

3. Sign language dataset

TRuleGrowth outpaced TNS, generating rules faster.

So, what's the takeaway? Your best bet depends on your specific needs. Think about:

Is your dataset sparse or dense?
How much memory do you have?
How fast do you need results?
What kind of output are you after?

Pick your method wisely, and you'll mine those patterns like a pro.

Real-World Uses and Examples

Fast mining of sequential patterns in big data isn't just theory. It's used in many industries. Let's look at some examples:

E-commerce: Predicting User Behavior

Online stores use this to guess what users will do next. A study on a nutrition store found:

Search terms alone were good at predicting add-to-basket actions
Using product categories didn't always help

Arthur Pitman, who worked on the study, said:

"Search terms alone are already quite effective for predicting users' add-to-basket actions. Using extra domain knowledge doesn't always improve accuracy."

Marketing: Understanding Customer Journeys

This helps marketers get how people shop online. A study showed:

Support values show how often a sequence happens
Advertisers can use demographics on platforms like Facebook
Example: Targeting married women for specific campaigns

Retail: Inventory Management

Warehouse managers use this to stock smart. Here's how:

A manager set these rules to look at 3 months of sales:

Rule	Value
Min gap between events	7 days
Max gap between events	30 days
Time window	2 days
Min support	40%

This helped the manager:

See weekly buying patterns
Stock up monthly based on 30-day patterns
Allow for 2-day gaps between related sales

Biology: Analyzing DNA

While we don't have specific examples, this method helps find patterns in DNA. It could help spot disease markers.

Future Developments and New Trends

The world of sequential pattern mining in big data is changing fast. Here's what's coming:

AI Integration

AI is shaking things up. It's making data mining quicker and more precise. How?

AI can spot patterns humans might miss
Machine learning adapts to changing data in real-time
Natural language processing digs deeper into text data

Edge Computing

Edge computing is moving data mining closer to the source. This means:

Less delay
Better real-time decisions
Easier handling of IoT data

Streaming Data Challenges

Real-time data is creating new hurdles:

Challenge	What It Means	Possible Fix
Endless Data	No clear endpoint	Better memory management
Many Sources	Juggling multiple inputs	Smarter data processing
Constant Change	Adapting on the fly	Flexible systems

Incremental Mining

Old methods are slow. New approaches focus on:

Mining only new or changed data
Updating patterns, not starting over

Take the IncTree-Miner algorithm. It uses a single threshold to control mining and handle concept drift.

Ethical Concerns

As AI gets more involved, ethics matter more. We'll need:

Explainable AI models
Ethical guidelines
Diverse data sets to reduce bias

Mixing with New Tech

Sequential pattern mining will likely team up with:

Blockchain for secure data handling
Quantum computing for complex tasks
IoT for gathering data from everywhere

The future? Faster, more accurate mining that's woven into business and research.

Conclusion

Sequential pattern mining in big data has evolved. It's faster and more practical now. Here's what you need to know:

AI is changing everything. Machine learning and NLP make data mining quicker and more precise.
Edge computing speeds things up. Processing data closer to the source means less delay and better real-time decisions.
New algorithms tackle big issues. The EWSPM algorithm, for instance, cuts down runtime and memory use.
Ethics are crucial. As AI grows, we need clear guidelines and diverse data to reduce bias.
It's not just for tech companies. Businesses across industries use these tools for insights and competitiveness.

Industry	Use Case	Impact
Finance	Fraud detection	Real-time prevention of suspicious transactions
Retail	Customer behavior analysis	Personalized recommendations, increased sales
Healthcare	Patient data analysis	Improved diagnoses, better treatment plans

Philippe Fournier-Viger, Professor of Computer Science, says:

"Performance is very important as one does not want to wait several hours to find patterns. However, considering the usefulness of the discovered patterns ensure that these algorithms will actually be used in real applications."

Looking ahead, we'll focus on making these tools more accessible and useful. The key? Keep learning, keep experimenting, and always think about how these patterns can solve real problems.

Fast Mining of Sequential Patterns in Big Data

Main Hurdles in Big Data Sequential Pattern Mining