QUERY AWARE DETERMINIZATION OF UNCERTAIN OBJECTS

QUERY AWARE DETERMINIZATION OF UNCERTAIN
OBJECTS
Abstract—This paper considers the problem of determinizing probabilistic data to
enable such data to be stored in legacy systems that accept only deterministic
input. Probabilistic data may be generated by automated data analysis/enrichment
techniques such as entity resolution, information extraction, and speech processing.
The legacy system may correspond to pre-existing web applications such as Flickr,
Picasa, etc. The goal is to generate a deterministic representation of probabilistic
data that optimizes the quality of the end-application built on deterministic data.
We explore such a determinization problem in the context of two different data
processing tasks—triggers and selection queries. We show that approaches such as
thresholding or top-1 selection traditionally used for determinization lead to
suboptimal performance for such applications. Instead, we develop a query-aware
strategy and show its advantages over existing solutions through a comprehensive
empirical evaluation over real and synthetic datasets.
EXISTING SYSTEM:
Determinizing Probabilistic Data. While we are not aware of any prior work that
directly addresses the issue of determinizing probabilistic data as studied in this
paper, the works that are very related to ours are . They explore how to determinize
answers to a query over a probabilistic database. In contrast, we are interested in

best deterministic representation of data (and not that of a answer to a query) so as
to continue to use existing end-applications that take only deterministic input. The
differences in the two problem settings lead to different challenges. Authors in
address a problem that chooses the set of uncertain objects to be cleaned, in order
to achieve the best improvement in the quality of query answers. However, their
goal is to improve quality of single query, while ours is to optimize quality of
overall query workload. Also, they focus on how to select the best set of objects
and each selected object is cleaned by human clarification, whereas we determinize
all objects automatically. These differences essentially lead to different
optimization challenges. Another related area is MAP inference in graphical
model, which aims to find the assignment to each variable that jointly maximizes
the probability defined by the model. The determinization problem for the cost-
based metric can be potentially viewed as an instance of MAP inference problem.
If we view the problem that way, the challenge becomes that of developing fast
and high-quality approximate algorithm for solving the corresponding NP-hard
problem. Section 3.3 exactly provides such algorithms, heavily optimized and
tuned to specifically our problem setting. Probabilistic Data Models. A variety of
advanced probabilistic data models have been proposed in the past. Our focus
however was determinizing probabilistic objects, such as image tags and speech
output, for which the probabilistic attribute model suffices. We note that
determining probabilistic data stored in more advanced probabilistic models such

as And/Xor tree might also be interesting. Extending our work to deal with data of
such complexity remains an interesting future direction of work.
PROPOSED SYSTEM:
Overall, the main contributions of this paper are:
• We introduce the problem of determinizing probabilistic data. Given a workload
of triggers/queries, the main challenge is to find the deterministic representation of
the data which would optimize certain quality metrics of the answer to these
triggers/queries.
• We propose a framework that solves the problem of determinization by
minimizing the expected cost of the answer to queries. We develop a branchand-
bound algorithm that finds an approximate near-optimal solution to the resulting
NP-hard problem.
• We address the problem of determinizing a collection of objects to optimize set-
based quality metrics, such as F-measure. We develop an efficient algorithm that
reaches near-optimal quality.
• We extend the solutions to handle a data model where mutual exclusion exists
among tags. We show that correlations among tags can be leveraged in our
solutions to get better results. We also demonstrate that our solutions are designed
to handle various types of queries.

• We empirically demonstrate that the proposed algorithms are very efficient and
reach high-quality results that are very close to those of the optimal solution. We
also demonstrate that they are robust to small changes in the original query
workload.
Module 1
Data Quality
Data quality refers to the level of quality of Data. There are many definitions of
data quality but data is generally considered high quality if, "they are fit for their
intended uses in operations, decision making and planning." (J. M. Juran).
Alternatively, data is deemed of high quality if it correctly represents the real-
world construct to which it refers. Furthermore, apart from these definitions, as
data volume increases, the question of internal consistency within data becomes
significant, regardless of fitness for use for any particular external purpose. The
people's views on data quality can often be in disagreement, even when discussing
the same set of data used for the same purpose. A considerable amount of data
quality research involves investigating and describing various categories of
desirable attributes (or dimensions) of data. These lists commonly
include accuracy, correctness, currency, completeness andrelevance. Nearly 200
such terms have been identified and there is little agreement in their nature (are
these concepts, goals or criteria?), their definitions or measures (Wang et al.,
1993). Software engineers may recognize this as a similar problem to "ilities".

Data quality control is the process of controlling the usage of data with known
quality measurements for an application or a process. This process is usually done
after a Data Quality Assurance (QA) process, which consists of discovery of data
inconsistency and correction.
Data QA processesprovides following information to Data Quality Control (QC):
 Severity of inconsistency
 Incompleteness
 Accuracy
 Precision
 Missing / Unknown
The Data QC process uses the information from the QA process to decide to use
the data for analysis or in an application or business process. For example, if a
Data QC process finds that the data contains too many errors or inconsistencies,
then it prevents that data from being used for its intended process which could
cause disruption. For example, providing invalid measurements from several
sensors to the automatic pilot feature on an aircraft could cause it to crash. Thus,
establishing data QC process provides the protection of usage of data control and
establishes safe information usage.
Module 2
Determinization Problem

Having defined the notation, we now can define the determinization problem:
Definition 1 (Determinization). Given a set of uncertain objects O = {O1,O2, . . .
,O|O|}, a query workload Q and a quality metric F, the goal of the deteriminization
problem is for each object O ∈ O to select from WO a set of tags AO ⊆ WO as the
deterministic representation of O, such that F(O,Q) is optimized.
The ground truth tags G are not associated with uncertain objects and thus not
known to any approach to the determinization problem. Therefore, such algorithms
cannot directly measure the quality F(O,Q) during their execution. Thus, in the
following section, we will present our solution that is based on maximizing the
expected quality measure E(F(O,Q)). Before we describe our solution to the
determinization problem, we note that a baseline algorithm for determinization is a
thresholding (threshold-cut) approach. The thresholding approach employs a pre-
defined threshold τ . For each object O it composes its answer set A by choosing wi
tags from W such that P(wi) ≥ τ . The advantage of this approach is that it is
computationally efficient and potentially can be tuned to a particular dataset O and
workload Q by changing τ . The main drawback of this approach is that it is
unaware of the query workload (“query-unaware") and thus does not necessarily
optimize the given quality metrics, which leads to lower quality.

Module 3
Branch and Bound Algorithm
Instead of performing a brute-force enumeration, we can employ a faster branch
and bound (BB) technique. The approach discovers answer sets in a greedy fashion
so that answer sets with lower costtend to be discovered first.
Outline of the BB algorithm
The algorithm uses a branch and bound strategy to explore a search tree. Each
node v in the tree corresponds to a partial tag selection where decisions of whether
to include certain tags in the answer set or not have been made. We capture the
partial selection of tags using the concept of sequence. The performance of the
algorithm depends upon the choice of the node (amongst all the non-leaf nodes) to
branch, the computation of upper and lower bounds of a node, and the choice of
the tag used to expand the node.We next describe these components of the
algorithm in detail.
Module 4
Node Selection

The algorithm maintains a priority queue H for picking a node v that contains the
most promising sequence Sv to continue with. Among the nodes in the priority
queue, which one should we choose to branch next? Let us consider sequence Sv
that corresponds to a node v ∈ H. For v we define Av as the set of answer
sequences that correspond to the leaf nodes derivable from node v via the
branching procedure described above. That is, Av corresponds to the leaf nodes of
the subtree rooted at node v. For example, or node v2 of the tree in Fig. 3, Av2 =
{Sv8 , Sv9 , Sv10 , Sv11 }. Then for node v let mv = min A∈ Av E(cost(A,G,Q)) be
the value of the minimum expected cost among these sequences. Notice that if we
knew the exact value of mv, it would have been an ideal key for the priority queue
H, since it would lead to the quickest way to find the best sequences. The problem
is that the exact value of mv is unknown when v is branched, since the subtree
rooted at v is not yet constructed at that moment. Even though mv is unknown it is
possible to quickly determine good lower and upper bounds on its value _v ≤ mv ≤
hv, without comparing scores of each sequence in Av. For any node v, if Sv is an
answer sequence then the bounds are equal to the cost of the sequence itself,
otherwise the bounds are computed as explained next.
Module 5
Query-Level Optimization

The performance of the BB algorithm can be significantly improved further by
employing query-level optimizations. For a given sequence Sv of node v, we might
be able to exactly determine the expected cost of certain queries (e.g., Cases 1 and
2 above), even if Sv is not an answer sequence. In other words, for these queries we
can get their cost without expanding Sv into the corresponding answer sequences A
∈ Av. We refer to such cost as fixed cost of v, and to such queries as fixed or
decided queries with respect to O. Thus, each node v is also associated with the set
of undecided queries Qv, its fixed cost ˆcv, as well as its lower and upper bounds
ˆlv and ˆhv on non-fixed cost. Note that lv = ˆcv +ˆlv and hv = ˆcv + ˆhv. Fixed cost
and fixed queries give us an opportunity to make the BB algorithm even more
efficient by reducing the number of queries involved in computing the expected
cost. When the algorithm performs a branching at a node, it creates two new nodes
vyes and vno, by making yes/no decision for one undecided tag w. Since one
additional tag is now decided for each new node v, it is possible that expected cost
for some query Q ∈ Qv can be computed (Case 1 and 2). If so, Q becomes a fixed
query for the new node and will not be considered in future computations derived
from that node. Based on the relation between Sv and Q, there are three different
cases for updating ˆcv, ˆlv and ˆhv
CONCLUSION:

In this paper we have considered the problem of determinizing uncertain objects to
enable such data to be stored in pre-existing systems, such as Flickr, that take only
deterministic input. The goal is to generate deterministic representations that
optimize the quality of answers to queries/triggers that execute over the
deterministic data representation. We have proposed efficient determinization
algorithms that are orders of magnitude faster than the enumeration based optimal
solution but achieves almost the same quality as the optimal solution. As future
work, we plan to explore determinization techniques in the context of applications,
wherein users are also interested in retrieving objects in a ranked order.
REFERENCES
[1] D. V. Kalashnikov, S. Mehrotra, J. Xu, and N. Venkatasubramanian, “A
semantics-based approach for speech annotation of images,” IEEE Trans. Knowl.
Data Eng., vol. 23, no. 9, pp. 1373–1387, Sept. 2011.
[2] J. Li and J. Wang, “Automatic linguistic indexing of pictures by a statistical
modeling approach,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp.
1075–1088, Sept. 2003.
[3] C. Wangand, F. Jing, L. Zhang, and H. Zhang, “Image annotation refinement
using random walk with restarts,” in Proc. 14th Annu. ACM Int. Conf. Multimedia,
New York, NY, USA, 2006.

[4] B. Minescu, G. Damnati, F. Bechet, and R. de Mori, “Conditional use of word
lattices, confusion networks and 1-best string hypotheses in a sequential
interpretation strategy,” in Proc. ICASSP, 2007.
[5] R. Nuray-Turan, D. V. Kalashnikov, S. Mehrotra, and Y. Yu, “Attribute and
object selection queries on objects with probabilistic attributes,” ACM Trans.
DatabaseSyst., vol. 37, no. 1, Article 3, Feb. 2012.
[6] J. Li and A. Deshpande, “Consensus answers for queries over probabilistic
databases,” in Proc. 28th ACM SIGMOD-SIGACTSIGART Symp. PODS, New
York, NY, USA, 2009.
[7] M. B. Ebarhimi and A. A. Ghorbani, “A novel approach for frequent phrase
mining in web search engine query streams,” in Proc. 5th Annu. Conf. CNSR,
Frederlcton, NB, Canada, 2007.
[8] S. Bhatia, D. Majumdar, and P. Mitra, “Query suggestions in the absence of
query logs,” in Proc. 34th Int. ACM SIGIR, Beijing, China, 2011.
[9] C. Manning and H. Schutze, Foundations of Statistical Natural Language
Processing, Cambridge, MA, USA: MIT Press, 1999.
[10] D. V. Kalashnikov and S. Mehrotra, “Domain-independent data cleaning via
analysis of entity-relationship graph,” ACM Trans. Database Syst., vol. 31, no. 2,
pp. 716–767, Jun. 2006.
[11] K. Schnaitter, S. Abiteboul, T. Milo, and N. Polyzotis, “On-line index
selection for shifting workloads,” in Proc. IEEE 23rd Int. Conf. Data Eng.
Workshop, Istanbul, Turkey, 2007.

[12] P. Unterbrunner, G. Giannikis, G. Alonso, D. Fauser, and D. Kossmann,
“Predictable performance for unpredictable workloads,” in Proc. VLDB, Lyon,
France, 2009.
[13] R. Cheng, J. Chen, and X. Xie, “Cleaning uncertain data with quality
guarantees,” in Proc. VLDB, Auckland, New Zealand, 2008.

QUERY AWARE DETERMINIZATION OF UNCERTAIN OBJECTS

More Related Content

What's hot (20)

Similar to QUERY AWARE DETERMINIZATION OF UNCERTAIN OBJECTS (20)

More from Nexgen Technology (20)

Recently uploaded (20)

QUERY AWARE DETERMINIZATION OF UNCERTAIN OBJECTS