Digital file size computational procedure in multimedia big data using sampling methodology

Abstract

The multimedia big data has tendency of fast growth over time span due to basic characteristics like volume, variety and velocity. Sample based estimates are used to compute the unknown population parameter. The multimedia big data is characterized various features who are prominent in terms of identification and analysis. Social media platforms are major sources of generating big data by virtue of communication among registered users in the form of text, video and images data. Registered users are also growing with drastic speed. When a user registers on a portal, default digital storage space is allotted by the system, who increases over time domain. A monitoring system is required to anticipate the increment and to alert managers of data centers for further enhancement of infrastructure. In the case of medical diagnostics, the CT-scan and MRI equipment produce the huge amount of scan files data while done over the large number of patients. These files are used to store in memory of the system for at least a prefixed duration. The digital file size of such reports, pixel densities and intensity of contents are the prime parameters of interest while comparing the quality of similar types of machines. Doctors and patients on social media platform used to exchange digital medical reports like X-ray, CT-scan, MRI, cancer diagnostics occupying the default storage. A guess value of digital file size can be helpful for the determination of expected amount of digital storage for users to be allocated to the medical processionals or other such. This paper presents sample based estimation methods for estimating the average file size over several time points. Confidence intervals are used as a tool of comparisons. A new simulation procedure is also suggested for comparative results of confidence intervals. At multiple optimum values of constant, the proposed sample based estimation methods perform better and the proposed simulation method is also result oriented. Strategy of using support information of other multimedia variable in estimation procedure is found useful and effective. Findings of the paper are numerically supported and significant percentage gain observed for proposed in the setup of multimedia big data floating over social media platforms.

Appendix A: Population N = 100

ID	Occasion I			Occasion II			Occasion III			Occasion IV			Occasion V			Occasion VI
	T	V	I	T	V	I	T	V	I	T	V	I	T	V	I	T	V	I
1	5	8	7	9	3	11	8	11	17	9	12	13	15	16	18	19	23	24
2	9	12	11	5	7	17	16	17	27	18	23	24	28	32	34	37	44	47
3	8	11	14	16	6	23	14	15	36	15	19	21	24	27	29	32	38	40
4	12	20	18	10	15	29	22	28	45	24	30	32	37	42	46	49	59	62
5	11	16	22	9	11	35	19	22	54	21	27	29	33	37	41	44	52	56
6	13	18	25	11	13	41	23	25	64	26	33	35	40	46	49	53	64	68
7	14	21	29	17	16	47	25	29	73	27	34	37	42	48	52	56	67	71
8	15	23	33	13	18	52	27	32	82	30	38	41	47	53	58	62	74	79
9	17	26	37	15	21	58	30	36	91	33	42	45	52	58	63	68	82	87
10	18	28	40	24	23	64	33	39	101	36	46	49	56	64	69	75	89	95
11	20	31	44	18	26	70	35	43	110	39	49	53	61	69	75	81	97	102
12	21	33	48	19	28	76	38	46	119	42	53	57	66	74	81	87	104	110
13	23	36	51	21	31	82	41	50	128	45	57	61	70	79	86	93	111	118
14	24	38	55	22	33	88	44	53	138	48	61	65	75	85	92	99	119	126
15	26	41	59	24	36	94	46	57	147	51	64	69	80	90	98	105	126	134
16	27	43	62	25	38	100	49	60	156	54	68	73	84	95	103	112	133	141
17	29	46	66	27	41	106	52	64	165	57	72	77	89	100	109	118	141	149
18	30	48	70	28	43	112	54	67	175	60	76	82	94	106	115	124	148	157
19	32	51	74	30	46	118	57	71	184	63	79	86	98	111	120	130	155	165
20	33	53	77	31	48	124	60	74	193	66	83	90	103	116	126	136	163	173
21	35	56	81	33	51	129	62	78	202	69	87	94	108	121	132	142	170	180
22	36	58	85	34	53	135	65	81	212	72	91	98	112	127	138	148	177	188
23	38	61	88	36	56	141	68	85	221	75	94	102	117	132	143	155	185	196
24	39	63	92	37	58	147	71	88	230	78	98	106	122	137	149	161	192	204
25	41	66	96	39	61	153	73	92	239	81	102	110	126	142	155	167	199	212
26	42	68	99	40	63	159	76	95	249	84	106	114	131	148	160	173	207	219
27	44	71	103	42	66	165	79	99	258	87	109	118	135	153	166	179	214	227
28	45	73	107	43	68	171	81	102	267	90	113	122	140	158	172	185	221	235
29	47	76	111	45	71	177	84	106	276	93	117	126	145	163	177	191	229	243
30	48	78	114	46	73	183	87	109	286	96	121	130	149	169	183	198	236	251
31	50	81	118	48	76	189	89	113	295	99	124	134	154	174	189	104	244	258
32	51	83	122	49	78	195	92	116	304	102	128	138	159	179	195	110	251	266
33	53	86	125	51	81	200	95	120	313	105	132	142	163	184	200	116	258	274
34	54	88	129	52	83	206	98	123	323	108	136	146	168	190	206	122	266	282
35	56	91	133	54	86	212	100	127	332	111	139	150	173	195	212	128	273	290
36	57	93	136	55	88	218	103	130	341	114	143	154	177	200	217	135	280	297
37	59	96	140	57	91	224	106	134	350	117	147	158	182	205	223	141	288	305
38	60	98	144	58	93	230	108	137	360	120	151	163	187	211	229	147	295	313
39	62	101	148	60	96	236	111	141	369	123	154	167	191	216	234	153	302	321
40	63	103	151	61	98	242	114	144	378	126	158	171	196	221	240	159	310	329
41	65	106	155	63	101	248	116	148	387	129	162	175	101	226	246	165	317	336
42	66	108	159	64	103	254	119	151	397	132	166	179	105	232	252	171	324	344
43	68	111	162	66	106	260	122	155	406	135	169	183	110	237	257	178	332	352
44	69	113	166	67	108	266	125	158	415	138	173	187	115	242	263	184	339	360
45	71	116	170	69	111	272	127	162	424	141	177	191	119	247	269	190	346	368
46	72	118	173	70	113	277	130	165	434	144	181	195	124	253	274	96	354	375
47	74	121	177	72	116	283	133	169	443	147	184	199	128	258	280	102	361	383
48	75	123	181	73	118	289	135	172	452	150	188	203	133	263	286	208	368	391
49	77	126	185	75	121	295	138	176	461	153	192	207	138	268	291	114	376	399
50	78	128	188	76	123	301	141	179	471	156	196	211	142	274	297	121	383	407
51	80	131	192	78	126	307	143	183	480	159	199	215	147	279	303	127	391	414
52	81	133	196	79	128	313	146	186	489	162	203	219	152	284	309	233	398	422
53	83	136	199	81	131	319	149	190	498	165	207	223	156	289	314	139	405	430
54	84	138	203	82	133	325	152	193	508	168	211	227	123	295	320	145	413	438
55	86	141	207	84	136	331	154	197	517	171	214	231	120	300	326	251	420	446
56	87	143	210	85	138	337	157	200	526	174	218	235	170	305	331	158	427	453
57	89	146	214	87	141	343	160	204	535	177	222	239	175	310	337	264	435	461
58	90	148	218	88	143	348	162	207	545	180	226	244	180	316	343	170	442	469
59	92	151	222	90	146	354	165	211	554	183	229	248	84	321	348	245	449	477
60	93	153	225	91	148	360	168	214	563	186	233	252	28	326	354	180	457	485
61	95	156	229	93	151	366	170	218	572	189	237	256	94	331	360	288	464	492
62	96	158	233	94	153	372	173	221	582	192	241	260	98	337	366	135	471	500
63	98	161	236	96	156	378	176	225	591	195	244	264	102	342	371	202	479	508
64	99	163	240	97	158	384	179	228	600	198	248	268	108	347	377	207	486	516
65	101	166	244	99	161	390	181	232	609	101	252	272	113	352	383	196	493	524
66	102	168	247	80	163	396	184	235	619	104	256	276	114	358	388	199	501	531
67	104	171	251	35	166	402	187	239	628	107	259	280	213	363	394	258	508	539
68	105	173	255	81	168	408	189	242	637	105	263	284	126	368	400	231	515	547
69	107	176	259	102	171	414	192	246	646	113	267	288	131	373	405	237	523	555
70	108	178	262	99	173	420	195	249	656	116	271	292	135	379	411	244	530	563
71	110	181	266	88	176	425	197	253	665	109	274	296	140	384	417	250	538	570
72	111	183	270	106	178	431	200	256	674	85	278	300	145	389	423	256	545	578
73	113	186	273	59	181	437	203	260	683	95	282	304	149	394	428	262	552	586
74	45	188	277	43	183	443	81	263	693	100	113	122	140	158	171	185	221	234
75	116	191	281	114	186	449	208	267	702	132	289	212	159	405	440	274	567	602
76	117	193	284	26	188	455	211	270	711	102	293	123	163	410	445	281	574	609
77	119	196	288	117	191	461	214	274	720	132	297	13	168	415	451	284	582	617
78	120	45	292	118	40	467	216	63	730	140	102	25	173	421	457	293	589	625
79	122	201	70	120	196	112	219	281	175	125	100	30	177	129	462	299	596	633
80	123	170	85	121	165	136	178	238	213	142	108	33	182	331	268	205	604	641
81	125	106	103	123	101	165	159	148	258	127	113	137	187	336	274	211	611	648
82	126	208	107	82	203	171	130	291	268	152	116	241	191	342	340	217	618	156
83	128	211	210	43	206	336	85	295	525	155	119	145	196	347	385	224	626	164
84	129	13	214	55	8	342	133	18	535	150	125	110	101	352	291	230	633	272
85	131	216	215	129	102	344	135	302	538	142	140	270	105	357	497	236	640	180
86	132	218	231	130	103	370	138	305	578	178	179	154	110	363	302	242	148	287
87	134	25	225	132	20	200	142	35	563	167	180	102	114	368	508	248	155	295
88	135	223	229	133	218	205	243	112	573	170	165	78	319	373	375	254	162	203
89	137	226	233	135	221	100	246	116	583	173	141	69	324	378	519	260	170	311
90	138	105	110	136	100	176	249	125	275	176	88	73	328	384	525	267	300	319
91	122	5	15	120	0	24	220	7	38	144	107	29	378	27	464	200	98	234
92	102	152	210	100	147	336	184	102	525	104	102	75	316	57	288	218	300	230
93	113	136	148	111	131	237	203	130	370	126	99	10	350	396	229	300	290	188
94	110	20	35	108	15	56	198	28	88	120	100	97	341	385	218	200	201	172
95	106	120	152	104	115	243	191	68	380	112	142	86	329	371	203	205	219	201
96	102	119	125	100	114	200	184	67	313	102	153	175	316	357	288	203	200	130
97	105	100	101	103	95	162	189	40	253	103	152	184	326	368	299	231	215	220
98	100	85	90	98	80	144	180	19	225	108	50	70	310	350	280	210	290	175
99	120	75	60	118	70	96	216	100	150	130	100	100	372	320	356	292	288	124
100	125	70	25	123	65	40	225	98	63	85	113	12	388	338	375	213	213	150

The population dataset and Python programming codes which we have used in this paper to calculate the results of each point of time is available at: https://abdulalim90.blogspot.com/