Modifying Transactional Databases to Hide Sensitive Association Rules

Although firms recognize the value in sharing data with supply chain partners, many remain reluctant to share for fear of sensitive information potentially making its way to competitors. Approaches that can help hide sensitive information could alleviate such concerns and increase the number of firms that are willing to share. Sensitive information in transactional databases often manifests itself in the form of association rules. The sensitive association rules can be concealed by altering transactions so that they remain hidden when the data are mined by the partner. The problem of hiding these rules in the data are computationally difficult (NP-hard), and extant approaches are all heuristic in nature. To our knowledge, this is the first paper that introduces the problem as a nonlinear integer formulation to hide the sensitive association rule while minimizing the alterations needed in the data set. We apply transformations that linearize the constraints and derive various results that help reduce the size of the problem to be solved. Our results show that although the nonlinear integer formulations are not practical, the linearizations and problem-reduction steps make a significant impact on solvability and solution time. This approach mitigates potential risks associated with sharing and should increase data sharing among supply chain partners.

Model to measure the effect of the integration of information in business processes. Example: bidding process for an industry

PurposeThe main contribution of the research is to propose a quantitative model based on indicators designed by the authors, to measure the effect on the performance of the process caused by an improvement in the integration of information.Design/methodology/approachIt is a descriptive and exploratory investigation, immersed in a case study; the bidding process was selected by senior management due to the high complexity and impact of this process on the organization. The protocol for carrying out the study involved interviews with the owner of the process and all the officials involved, as sources of information, inputs to model in BPMN2.0 at the activity level and a statistical validation of the model was carried out, to sustain an acceptable confidence of the same.FindingsAmong the most outstanding results of the research are that the effects of the improvements or changes in the activities were reflected or evidenced in subsequent activities, not in the immediate ones or in which the improvement is applied. This confirms the theorization of the systemic approach to processes since the effects of the improvements are not close to their application.Research limitations/implicationsFor the proposed model, it was evident that the measurement of the efficiency and quality dimensions of the process activities required verification in transactional databases where nonconformities and nonquality costs related to orders are recorded.Originality/valueIn the reviewed literature, there are no models or reference frameworks that quantitatively measure the effects of the integration of information on the performance of processes.

Improving Association Rule Mining Using Clustering-Based Data Mining Model for Traffic Accidents

Data mining is a process of knowledge discovery to extract the interesting, previously unknown, potentially useful, and nontrivial patterns from large data sets. Currently, there is an increasing interest in data mining in traffic accidents, which makes it a growing new research community. A large number of traffic accidents in recent years have generated large amounts of traffic accident data. The mining algorithms had a great role in determining the causes of these accidents, especially the association rule algorithms. One challenging problem in data mining is effective association rules mining with the huge transactional databases, many efforts have been made to propose and improve association rules mining methods. In the paper, we use the RapidMiner application to design a process that can generate association rules based on clustering algorithms.

Zen

Emerging <u>N</u>on-<u>V</u>olatile <u>M</u>emory (NVM) technologies like 3DX-point promise significant performance potential for OLTP databases. However, transactional databases need to be redesigned because the key assumptions that non-volatile storage is orders of magnitude slower than DRAM and only supports blocked-oriented access have changed. NVMs are byte-addressable and almost as fast as DRAM. The capacity of NVM is much (4-16x) larger than DRAM. Such NVM characteristics make it possible to build OLTP database entirely in NVM main memory. This paper studies the structure of OLTP engines with hybrid NVM and DRAM memory. We observe three challenges to design an OLTP engine for NVM: tuple metadata modifications, NVM write redundancy, and NVM space management. We propose Zen, a high-throughput log-free OLTP engine for NVM. Zen addresses the three design challenges with three novel techniques: metadata enhanced tuple cache, log-free persistent transactions, and light-weight NVM space management. Experimental results on a real machine equipped with Intel Optane DC Persistent Memory show that Zen achieves up to 10.1x improvement compared with existing solutions to run an OLTP database as large as the size of NVM while achieving fast failure recovery.

The selection of the relevant association rules using the ELECTRE method with multiple criteria

The extraction of association rules is a very attractive data mining task and the most widespread in the business world and in modern society, trying to obtain the interesting relationship and connection between collections of articles, products or items in high transactional databases. The immense quantity of association rules obtained expresses the main obstacle that a decision maker can handle. Consequently, in order to establish the most interesting association rules, several interestingness measures have been introduced. Currently, there is no optimal measure that can be chosen to judge the selected association rules. To avoid this problem we suggest to apply ELECTRE method one of the multi-criteria decision making, taking into consideration a formal study of measures of interest according to structural properties, and intending to find a good compromise and select the most interesting association rules without eliminating any measures. Experiments conducted on reference data sets show a significant improvement in the performance of the proposed strategy.

Data protection in transactional and statistical applications of databases

The article describes a discussion on the issue of data protection in databases. The discussion attempts to answer the question about the possibility of using a transactional database system as a system capable of data protection in a statistical database. The discussion is preceded by a reminder of the basic issues related to data protection in databases, including reminder of flow control models, access control models and the inference. The key element of the article is the analysis, based on the example of the Oracle database management system, whether data protection mechanisms in transactional databases can be effective in case of data protection in statistical databases.