The fraudulent traffic and clicks can result in significant revenue loss for mobile ad networks. Advertisers pay for ad impressions and clicks that are not actually driving real business value, and the mobile ad network is unable to monetize this wasted ad spend:
- Bot Traffic: Bot traffic refers to fake traffic generated by automated scripts. This traffic is often indistinguishable from real traffic, making it difficult to detect. Bot traffic can artificially inflate ad impressions, clicks, and conversions, leading to wasted ad spend.
- Click Fraud: Click fraud occurs when a user or automated script clicks on an ad without any intention of interacting with the ad or visiting the advertiser’s website. This type of fraud results in an increase in ad impressions and clicks, but no actual conversions.
- Invalid Traffic: Invalid traffic refers to traffic that is generated from sources that are not relevant to the advertiser’s target audience. This can include users who click on an ad by accident or traffic generated from malware-infected devices.
To minimize revenue leakage, it is important for mobile ad networks to implement robust fraud detection systems that can accurately identify and eliminate fraudulent traffic and clicks.
How MinervaDB can help Mobile Ad. Networks for implementing real-time fraud detection systems?
Probabilistic data structures, such as bloom filters, are used in real-time fraud detection systems because they provide a way to quickly identify if an element is definitely not in a set or may be in the set. This makes them ideal for use in fraud detection systems, where time is of the essence.
In the context of a mobile ad network, bloom filters can be used to detect fraudulent traffic and clicks. When a request is made to display an ad, the system can check the bloom filter to see if the requestor is known to be a fraudulent source. If the requestor is not in the set, then the request can be processed as normal. If the requestor is in the set, then the request can be flagged as potentially fraudulent and subjected to further analysis.
Bloom filters can be updated in real-time as new information becomes available. This means the fraud detection system can be updated with new information as it is discovered, improving its accuracy over time. Also, bloom filters are memory-efficient, making them well-suited for use in systems with large amounts of data.
How RocksDB works?
RocksDB is an open-source, embedded, persistent key-value store for fast storage. It was developed by Facebook and is designed to run on flash and memory storage devices. RocksDB provides low-latency, high-throughput access to stored data, making it suitable for use in high-performance, low-latency systems.
Bloom filters are probabilistic data structures that can be used to determine if an element is a member of a set. In RocksDB, bloom filters are used to reduce the number of disk accesses required to determine if a key is present in the database. A bloom filter is a compact representation of a set that can quickly determine whether an element is definitely not in the set or might be in the set. This can reduce the number of disk accesses required, which can result in significant performance improvements for certain workloads.
MyRocks is a high-performance, space-efficient storage engine for the MySQL database management system based on RocksDB. MyRocks was developed to provide a storage engine alternative to the InnoDB storage engine that is included with MySQL. MyRocks provides features such as bloom filters and data compression to improve performance further and reduce storage requirements.
How to use MyRocks with bloom filters for real-time Fraud Detection System implementation in Mobile Advertisement Networks?
Bloom filters are used in Mobile Ad. Tech Database Servers using MyRocks efficiently filter out unwanted data irrelevant to a particular query. This can significantly improve the database server’s performance by reducing the amount of data that needs to be processed for each query.
In mobile ad tech, bloom filters can be used to filter out unwanted impressions, clicks, and conversions that do not match certain criteria, such as target audience demographics or geographic location. Using a bloom filter, the database server can quickly eliminate these irrelevant data points and only process data relevant to the query, reducing the overall load on the database server and improving query performance.
Additionally, bloom filters can be used in mobile ad tech to prevent fraud by checking if a user has already seen an ad, ensuring that a user is not charged multiple times for a single impression. By using bloom filters to check for duplicates, mobile ad tech companies can ensure that their billing systems are accurate and that they are not overcharging advertisers for duplicate impressions.
In the context of a mobile ad network, bloom filters can be used to detect fraudulent traffic and clicks. When a request is made to display an ad, the system can check the bloom filter to see if the requestor is known to be a fraudulent source. If the requestor is not in the set, then the request can be processed as normal. If the requestor is in the set, then the request can be flagged as potentially fraudulent and subjected to further analysis.
Bloom filters can be updated in real-time as new information becomes available. This means that the fraud detection system can be updated with new information as it is discovered, improving its accuracy over time. Also, bloom filters are memory-efficient, making them well-suited for use in systems with large amounts of data.
How is Bloom Filters implemented in MyRocks?
In MyRocks, bloom filters are implemented as part of the storage engine. They are used to improve the performance of queries by reducing the number of disk I/O operations. MyRocks uses bloom filters to determine if a key is in a given SSTable (sorted string table) or not. If the key is not in the SSTable, the engine can skip reading the data from disk, which is much faster than reading the data from disk and then finding out that the key is not there.
Bloom filters are a probabilistic data structure used in databases to test whether an element is a member of a set or not. In MyRocks, bloom filters are used to improve query performance by reducing the number of unnecessary disk lookups.
MinervaDB Benefits
- Vendor-neutral and independent, Enterprise-class consulting, 24*7 support and remote DBA services for MySQL, MariaDB, MyRocks, PostgreSQL and ClickHouse.
- A virtual corporation with a global team of seasoned professionals – We have consultants operating from multiple locations worldwide; all of us work from home and stay connected via email, Google Hangouts, Skype, private IRC, WhatsApp, Telegram and phone. Being a virtual corporation we can hire the best talent from anywhere in the world, This makes an truly 24*7 operational team.
- Competitive pricing – We are a virtual corporation so we don’t charge the customers for our infrastructure cost; what you pay us goes purely for our unmatched technology team.
- We operate 24*7 – Our team operates from multiple locations worldwide so we are available 24*7.
- Cloud DBA Services – IaaS and DBaaS including: Oracle Cloud, Google CloudSQL, Amazon Aurora, AWS RDS®, EC2®, Microsoft Azure® and Rackspace® Cloud.
- Pay As You Go billing model – You pay us only for hours worked. We don’t ask for advances ever !! We are committed to delivering cost-efficient consulting, support and services for our customers globally.
- Transparent ticketing system – We share with you the detailed work report of what we have done for your database infrastructure; this also includes how you will get benefitted from the change we have made. We love absolute transparency and detailed documentation.
- Emergency support is available for you even when you are not our customer; emergency support channels – Email, Slack, Google Hangouts, Skype and Phone.
- Pay-per-incident option available – Do you need our support fixing a single incident? No problem, We have that option available.
Conclusion
Bloom Filters are a useful tool in mobile ad tech databases that use MyRocks, as they can improve performance by reducing the amount of data that needs to be processed for each query and prevent fraud by checking for duplicates.
